Patent Grant
11984123
The present technology relates to consumer goods and, more particularly, to methods, systems, products, features, services, and other elements directed to voice-assisted control of media playback systems or some aspect thereof.
Options for accessing and listening to digital audio in an out-loud setting were limited until in 2002, when SONOS, Inc. began development of a new type of playback system. Sonos then filed one of its first patent applications in 2003, entitled “Method for Synchronizing Audio Playback between Multiple Networked Devices,” and began offering its first media playback systems for sale in 2005. The Sonos Wireless Home Sound System enables people to experience music from many sources via one or more networked playback devices. Through a software control application installed on a controller (e.g., smartphone, tablet, computer, voice input device), one can play what she wants in any room having a networked playback device. Media content (e.g., songs, podcasts, video sound) can be streamed to playback devices such that each room with a playback device can play back corresponding different media content. In addition, rooms can be grouped together for synchronous playback of the same media content, and/or the same media content can be heard in all rooms synchronously.
Features, aspects, and advantages of the presently disclosed technology may be better understood with regard to the following description, appended claims, and accompanying drawings where:
Features, aspects, and advantages of the presently disclosed technology may be better understood with regard to the following description, appended claims, and accompanying drawings, as listed below. A person skilled in the relevant art will understand that the features shown in the drawings are for purposes of illustrations, and variations, including different and/or additional features and arrangements thereof, are possible.
The drawings are for purposes of illustrating example embodiments, but it should be understood that the inventions are not limited to the arrangements and instrumentality shown in the drawings. In the drawings, identical reference numbers identify at least generally similar elements. To facilitate the discussion of any particular element, the most significant digit or digits of any reference number refers to the Figure in which that element is first introduced. For example, element 103a is first introduced and discussed with reference to
Examples described herein involve techniques to trigger voice assistant(s) on a network microphone device (NMD). An NMD is a networked computing device that typically includes an arrangement of microphones, such as a microphone array, that is configured to detect sound present in the NMD's environment. In some examples, an NMD may be implemented within another device, such as an audio playback device. Once the voice assistant is triggered, the NMD may start recording voice input as a potential voice command.
A user may utilize different techniques based on their distance from the NMD. Some of these techniques are “wake-wordless” in that they involve triggering the voice assistant without the user having to speak an explicit wake-word. Instead, the NMD may trigger the voice assistant based on two or more factors, such as one or more physical conditions and the presence of voice activity.
In a first range, a user may trigger voice assistant(s) on an NMD using a combination of voice and touch. In an example, a housing of the NMD (or a portion thereof, e.g., more than 50%) is touch sensitive (e.g., capacitive). User gestures and other motions to come into contact or close proximity with the housing of the NMD may be interpreted as an intent to address the voice assistant. As such, the NMD may start listening for voice activity and/or lower one or more thresholds for interpreting voice activity as a voice input to the voice assistant(s) based on detecting such a gesture or motion via the housing of the NMD. Notably, the housing of the NMD may also carry a button that, when pressed, explicitly triggers the voice assistant(s).
In a second range, a user may trigger the voice assistant(s) on the NMD using a combination of touch or line-of-sight. Line-of-sight may be detected visually (e.g., via one or more cameras carried in the housing of the NMD, which detect eye contact and/or other positioning of the user) or aurally (e.g., via an analysis of the user's voice as captured by a microphone array carried in the housing of the NMD to determine whether the user was facing in the direction of the NMD when speaking). Line-of-sight (that is, a user looking at an NMD or speaking at an NMD) may be indicative of a user intending to invoke a voice assistant. Similar to detection in the first range, the NMD may start listening for voice activity and/or lower one or more thresholds for interpreting voice activity as a voice input to the voice assistant(s) based on detecting that the user is in line-of-sight to the NMD.
In a third range (e.g., far, or out of line-of-sight), a user may trigger the voice assistant(s) on the NMD using a wake word, which is a more conventional technique of triggering voice assistants. A voice input to an NMD will typically include a wake word followed by an utterance comprising a user request. In practice, a wake word is typically a predetermined nonce word or phrase used to “wake up” an NMD and cause it to invoke a particular voice assistant service (“VAS”) to interpret the intent of voice input in detected sound. For example, a user might speak the wake word “Alexa” to invoke the AMAZON® VAS, “Ok, Google” to invoke the GOOGLE® VAS, “Hey, Siri” to invoke the APPLE® VAS, or “Hey, Sonos” to invoke a VAS offered by SONOS®, among other examples. A wake word may also be referred to as, for example, an activation-, trigger-, wakeup-word or -phrase, and may take the form of any suitable word, combination of words (e.g., a particular phrase), and/or some other audio cue.
To identify whether sound detected by the NMD contains a voice input that includes a particular wake word, NMDs often utilize a wake-word engine, which is typically onboard the NMD. The wake-word engine may be configured to identify (i.e., “spot” or “detect”) a particular wake word in recorded audio using one or more identification algorithms. Such identification algorithms may include pattern recognition trained to detect the frequency and/or time domain patterns that speaking the wake word creates. This wake-word identification process is commonly referred to as “keyword spotting.” In practice, to help facilitate keyword spotting, the NMD may buffer sound detected by a microphone of the NMD and then use the wake-word engine to process that buffered sound to determine whether a wake word is present in the recorded audio.
When a wake-word engine detects a wake word in recorded audio, the NMD may determine that a wake-word event (i.e., a “wake-word trigger”) has occurred, which indicates that the NMD has detected sound that includes a potential voice input. The occurrence of the wake-word event typically causes the NMD to perform additional processes involving the detected sound. With a VAS wake-word engine, these additional processes may include extracting detected-sound data from a buffer, among other possible additional processes, such as outputting an alert (e.g., an audible chime and/or a light indicator) indicating that a wake word has been identified. Extracting the detected sound may include reading out and packaging a stream of the detected-sound according to a particular format and transmitting the packaged sound-data to an appropriate VAS for interpretation.
In turn, the VAS corresponding to the wake word that was identified by the wake-word engine receives the transmitted sound data from the NMD over a communication network. A VAS traditionally takes the form of a remote service implemented using one or more cloud servers configured to process voice inputs (e.g., AMAZON's ALEXA, APPLE's SIRI, MICROSOFT's CORTANA, GOOGLE'S ASSISTANT, etc.). In some instances, certain components and functionality of the VAS may be distributed across local and remote devices.
When a VAS receives detected-sound data, the VAS processes this data, which involves identifying the voice input and determining intent of words captured in the voice input. The VAS may then provide a response back to the NMD with some instruction according to the determined intent. Based on that instruction, the NMD may cause one or more smart devices to perform an action. For example, in accordance with an instruction from a VAS, an NMD may cause a playback device to play a particular song or an illumination device to turn on/off, among other examples. In some cases, an NMD, or a media system with NMDs (e.g., a media playback system with NMD-equipped playback devices) may be configured to interact with multiple VASes. In practice, the NMD may select one VAS over another based on the particular wake word identified in the sound detected by the NMD.
One challenge with traditional wake-word engines is that they can be prone to false positives caused by “false wake word” triggers. A false positive in the NMD context generally refers to detected sound input that erroneously invokes a VAS. With a VAS wake-work engine, a false positive may invoke the VAS, even though there is no user actually intending to speak a wake word to the NMD.
For example, a false positive can occur when a wake-word engine identifies a wake word in detected sound from audio (e.g., music, a podcast, etc.) playing in the environment of the NMD. This output audio may be playing from a playback device in the vicinity of the NMD or by the NMD itself. For instance, when the audio of a commercial advertising AMAZON's ALEXA service is output in the vicinity of the NMD, the word “Alexa” in the commercial may trigger a false positive. A word or phrase in output audio that causes a false positive may be referred to herein as a “false wake word.”
In other examples, words that are phonetically similar to an actual wake word cause false positives. For example, when the audio of a commercial advertising LEXUS® automobiles is output in the vicinity of the NMD, the word “Lexus” may be a false wake word that causes a false positive because this word is phonetically similar to “Alexa.” As other examples, false positives may occur when a person speaks a VAS wake word or phonetically similar word in conversation.
The occurrences of false positives are undesirable, as they may cause the NMD to consume additional resources or interrupt audio playback, among other possible negative consequences. Some NMDs may avoid false positives by requiring a button press to invoke the VAS, such as on the AMAZON FIRETV remote or the APPLE TV remote. In practice, the impact of a false positive generated by a VAS wake-word engine is often partially mitigated by the VAS processing the detected-sound data and determining that the detected-sound data does not include a recognizable voice input.
As noted above, example techniques in the first and second ranges may invoke a voice assistant without the user speaking a pre-determined nonce wake word. Instead, the user may utilize a combination of a physical intent (e.g., a gesture to the housing of the NMD or line-of-sight) as well as one or more keywords. These keywords may be a word or a combination of words (e.g., a phrase) that functions as a command itself, such as a playback command. For this reason, keywords are also referred to herein as command keywords. In this manner, the keywords, along with other factors, may function as both an activation trigger and the command itself (or a portion thereof).
Within examples, a NMD may enable a wakewordless mode when a trigger is detected in the first or second range. In such a mode, the NMD may monitor for voice inputs that do not necessarily include a wake word. Instead, such voice input may include keywords.
One advantage of a keyword engine is that the recorded audio does not necessarily need to be sent to a VAS for processing, which may result in a quicker response to the voice input as well as increased user privacy, among other possible benefits. In some implementations described below, a detected keyword event may cause one or more subsequent actions, such as local natural language processing of a voice input. In some implementations, a keyword event may be one condition among one or more other conditions that must be detected before causing such actions.
According to example techniques described herein, after detecting a keyword, example NMDs may generate a keyword event (and perform a command corresponding to the detected keyword(s)) only when certain conditions corresponding to the detected command keyword are met. For instance, the NMD may perform a command only when the keywords are detected with at least a confidence level. As noted above, if other physical conditions are present, such as line-of-sight or touch, these threshold may be lowered on the assumption that these factors indicate a user's intent to invoke the voice assistant(s).
The specific degrees of threshold lowering may be different for the different physical conditions. For instance, since the touch is more deliberate, the threshold may be lowered by a first amount (e.g., from 90% confidence to 70%) when touch is detected in the presence of voice. Since line-of-sight is not necessarily as deliberate, the threshold may be lowered by a second, lesser amount (e.g., from 90% confidence to 80%) when line-of-sight is detected in the presence of voice.
The physical conditions may be used in combination with other conditions, such as command conditions indicating that the NMD is in a state to perform the condition. For instance, after detecting the command keyword “skip,” an example NMD generates a keyword event (and skips to the next track) only when certain playback conditions indicating that a skip should be performed are met. These playback conditions may include, for example, (i) a first condition that a media item is being played back, (ii) a second condition that a queue is active, and (iii) a third condition that the queue includes a media item subsequent to the media item being played back. If any of these conditions are not satisfied, the command keyword event is not generated (and no skip is performed).
By requiring both (a) detection of a keyword and (b) certain conditions before generating a keyword event, the prevalence of false positives may be reduced. For instance, when playing TV audio, dialogue or other TV audio would not have the potential to generate false positives for the “skip” command keyword since the TV audio input is active (and not a queue). Moreover, the NMD can continually listen for keywords (rather than requiring a button press to put the NMD in condition to receive a voice input) as the conditions relating to the state of the controlled device gate wake word event generation.
Aspects of conditioning keyword events may also be applicable to VAS wake-word engines and other traditional nonce wake-word engines. For example, such conditioning can possibly make practicable other wake word engines in addition to command keyword engines that might otherwise be prone to false positives.
Further, a keyword may be a single word or a phrase. Phrases generally include more syllables, which generally make the keyword more unique and easier to identify by the command keyword engine. Accordingly, in some cases, keywords that are phrases may be less prone to false positive detections. Further, using a phrase may allow more intent to be incorporated into the command keyword. For instance, a command keyword of “skip forward” signals that a skip should be forward in a queue to a subsequent track, rather than backward to a previous track.
Yet further, an NMD may include a local natural language unit (NLU). In contrast to a NLU implemented in one or more cloud servers that is capable of recognizing a wide variety of voice inputs, example local NLUs are capable of recognizing a relatively small library of keywords (e.g., 10,000 words and phrases), which facilitates practical implementation on the NMD. When the command keyword engine generates a command keyword event after detecting a command keyword in a voice input, the local NLU may process a voice utterance portion of the voice input to look for keywords from the library and determine an intent from the found keywords.
If the voice utterance portion of the voice input includes at least one keyword from the library, the NMD may perform the command corresponding to the command keyword according to one or more parameters corresponding to the least one keyword. In other words, the keywords may alter or customize the command corresponding to the command keyword. For instance, the command keyword engine may be configured to detect “play” as a command keyword and the local NLU library could include the phrase “low volume.” Then, if the user speaks “Play music at low volume” as a voice input, the command keyword engine generates a command keyword event for “play” and uses the keyword “low volume” as a parameter for the “play” command. Accordingly, the NMD not only causes playback based on this voice input, but also lowers the volume.
One possible advantage of a local NLU is increased privacy. By processing voice utterances locally, a user may avoid transmitting voice recordings to the cloud (e.g., to servers of a voice assistant service). Further, in some implementations, the NMD may use a local area network to discover playback devices and/or smart devices connected to the network, which may avoid providing this data to the cloud. Also, the user's preferences and customizations may remain local to the NMD(s) in the household, perhaps only using the cloud as an optional backup. Other advantages are possible as well.
While some embodiments described herein may refer to functions performed by given actors, such as “users” and/or other entities, it should be understood that this description is for purposes of explanation only. The claims should not be interpreted to require action by any such example actor unless explicitly required by the language of the claims themselves.
Moreover, some functions are described herein as being performed “based on” or “in response to” another element or function. “Based on” should be understood that one element or function is related to another function or element. “In response to” should be understood that one element or function is a necessary result of another function or element. For the sake of brevity, functions are generally described as being based on another function when a functional link exists; however, such disclosure should be understood as disclosing either type of functional relationship.
Within these rooms and spaces, the MPS 100 includes one or more computing devices. Referring to
With reference still to
As further shown in
In some implementations, the various playback devices, NMDs, and/or controller devices 102-104 may be communicatively coupled to at least one remote computing device associated with a VAS and at least one remote computing device associated with a media content service (“MCS”). For instance, in the illustrated example of
As further shown in
In various implementations, one or more of the playback devices 102 may take the form of or include an on-board (e.g., integrated) network microphone device. For example, the playback devices 102a-e include or are otherwise equipped with corresponding NMDs 103a-e, respectively. A playback device that includes or is equipped with an NMD may be referred to herein interchangeably as a playback device or an NMD unless indicated otherwise in the description. In some cases, one or more of the NMDs 103 may be a stand-alone device. For example, the NMDs 103f and 103g may be stand-alone devices. A stand-alone NMD may omit components and/or functionality that is typically included in a playback device, such as a speaker or related electronics. For instance, in such cases, a stand-alone NMD may not produce audio output or may produce limited audio output (e.g., relatively low-quality audio output).
The various playback and network microphone devices 102 and 103 of the MPS 100 may each be associated with a unique name, which may be assigned to the respective devices by a user, such as during setup of one or more of these devices. For instance, as shown in the illustrated example of
As discussed above, an NMD may detect and process sound from its environment, such as sound that includes background noise mixed with speech spoken by a person in the NMD's vicinity. For example, as sounds are detected by the NMD in the environment, the NMD may process the detected sound to determine if the sound includes speech that contains voice input intended for the NMD and ultimately a particular VAS. For example, the NMD may identify whether speech includes a wake word associated with a particular VAS.
In the illustrated example of
Upon receiving the stream of sound data, the VAS 190 determines if there is voice input in the streamed data from the NMD, and if so the VAS 190 will also determine an underlying intent in the voice input. The VAS 190 may next transmit a response back to the MPS 100, which can include transmitting the response directly to the NMD that caused the wake-word event. The response is typically based on the intent that the VAS 190 determined was present in the voice input. As an example, in response to the VAS 190 receiving a voice input with an utterance to “Play Hey Jude by The Beatles,” the VAS 190 may determine that the underlying intent of the voice input is to initiate playback and further determine that intent of the voice input is to play the particular song “Hey Jude.” After these determinations, the VAS 190 may transmit a command to a particular MCS 192 to retrieve content (i.e., the song “Hey Jude”), and that MCS 192, in turn, provides (e.g., streams) this content directly to the MPS 100 or indirectly via the VAS 190. In some implementations, the VAS 190 may transmit to the MPS 100 a command that causes the MPS 100 itself to retrieve the content from the MCS 192.
In certain implementations, NMDs may facilitate arbitration amongst one another when voice input is identified in speech detected by two or more NMDs located within proximity of one another. For example, the NMD-equipped playback device 102d in the environment 101 (
In certain implementations, an NMD may be assigned to, or otherwise associated with, a designated or default playback device that may not include an NMD. For example, the Island NMD 103f in the Kitchen 101h (
Further aspects relating to the different components of the example MPS 100 and how the different components may interact to provide a user with a media experience may be found in the following sections. While discussions herein may generally refer to the example MPS 100, technologies described herein are not limited to applications within, among other things, the home environment described above. For instance, the technologies described herein may be useful in other home environment configurations comprising more or fewer of any of the playback, network microphone, and/or controller devices 102-104. For example, the technologies herein may be utilized within an environment having a single playback device 102 and/or a single NMD 103. In some examples of such cases, the NETWORK 111 (
a. Example Playback & Network Microphone Devices
As shown, the playback device 102 includes at least one processor 212, which may be a clock-driven computing component configured to process input data according to instructions stored in memory 213. The memory 213 may be a tangible, non-transitory, computer-readable medium configured to store instructions that are executable by the processor 212. For example, the memory 213 may be data storage that can be loaded with software code 214 that is executable by the processor 212 to achieve certain functions.
In one example, these functions may involve the playback device 102 retrieving audio data from an audio source, which may be another playback device. In another example, the functions may involve the playback device 102 sending audio data, detected-sound data (e.g., corresponding to a voice input), and/or other information to another device on a network via at least one network interface 224. In yet another example, the functions may involve the playback device 102 causing one or more other playback devices to synchronously playback audio with the playback device 102. In yet a further example, the functions may involve the playback device 102 facilitating being paired or otherwise bonded with one or more other playback devices to create a multi-channel audio environment. Numerous other example functions are possible, some of which are discussed below.
As just mentioned, certain functions may involve the playback device 102 synchronizing playback of audio content with one or more other playback devices. During synchronous playback, a listener may not perceive time-delay differences between playback of the audio content by the synchronized playback devices. U.S. Pat. No. 8,234,395 filed on Apr. 4, 2004, and titled “System and method for synchronizing operations among a plurality of independently clocked digital data processing devices,” which is hereby incorporated by reference in its entirety, provides in more detail some examples for audio playback synchronization among playback devices.
To facilitate audio playback, the playback device 102 includes audio processing components 216 that are generally configured to process audio prior to the playback device 102 rendering the audio. In this respect, the audio processing components 216 may include one or more digital-to-analog converters (“DAC”), one or more audio preprocessing components, one or more audio enhancement components, one or more digital signal processors (“DSPs”), and so on. In some implementations, one or more of the audio processing components 216 may be a subcomponent of the processor 212. In operation, the audio processing components 216 receive analog and/or digital audio and process and/or otherwise intentionally alter the audio to produce audio signals for playback.
The produced audio signals may then be provided to one or more audio amplifiers 217 for amplification and playback through one or more speakers 218 operably coupled to the amplifiers 217. The audio amplifiers 217 may include components configured to amplify audio signals to a level for driving one or more of the speakers 218.
Each of the speakers 218 may include an individual transducer (e.g., a “driver”) or the speakers 218 may include a complete speaker system involving an enclosure with one or more drivers. A particular driver of a speaker 218 may include, for example, a subwoofer (e.g., for low frequencies), a mid-range driver (e.g., for middle frequencies), and/or a tweeter (e.g., for high frequencies). In some cases, a transducer may be driven by an individual corresponding audio amplifier of the audio amplifiers 217. In some implementations, a playback device may not include the speakers 218, but instead may include a speaker interface for connecting the playback device to external speakers. In certain embodiments, a playback device may include neither the speakers 218 nor the audio amplifiers 217, but instead may include an audio interface (not shown) for connecting the playback device to an external audio amplifier or audio-visual receiver.
In addition to producing audio signals for playback by the playback device 102, the audio processing components 216 may be configured to process audio to be sent to one or more other playback devices, via the network interface 224, for playback. In example scenarios, audio content to be processed and/or played back by the playback device 102 may be received from an external source, such as via an audio line-in interface (e.g., an auto-detecting 3.5 mm audio line-in connection) of the playback device 102 (not shown) or via the network interface 224, as described below.
As shown, the at least one network interface 224, may take the form of one or more wireless interfaces 225 and/or one or more wired interfaces 226. A wireless interface may provide network interface functions for the playback device 102 to wirelessly communicate with other devices (e.g., other playback device(s), NMD(s), and/or controller device(s)) in accordance with a communication protocol (e.g., any wireless standard including IEEE 802.11a, 802.11b, 802.11g, 802.11n, 802.11ac, 802.15, 4G mobile communication standard, and so on). A wired interface may provide network interface functions for the playback device 102 to communicate over a wired connection with other devices in accordance with a communication protocol (e.g., IEEE 802.3). While the network interface 224 shown in
In general, the network interface 224 facilitates data flow between the playback device 102 and one or more other devices on a data network. For instance, the playback device 102 may be configured to receive audio content over the data network from one or more other playback devices, network devices within a LAN, and/or audio content sources over a WAN, such as the Internet. In one example, the audio content and other signals transmitted and received by the playback device 102 may be transmitted in the form of digital packet data comprising an Internet Protocol (IP)-based source address and IP-based destination addresses. In such a case, the network interface 224 may be configured to parse the digital packet data such that the data destined for the playback device 102 is properly received and processed by the playback device 102.
As shown in
In operation, the voice-processing components 220 are generally configured to detect and process sound received via the microphones 222, identify potential voice input in the detected sound, and extract detected-sound data to enable a VAS, such as the VAS 190 (
As further shown in
In some implementations, the power components 227 of the playback device 102 may additionally include an internal power source 229 (e.g., one or more batteries) configured to power the playback device 102 without a physical connection to an external power source. When equipped with the internal power source 229, the playback device 102 may operate independent of an external power source. In some such implementations, the external power source interface 228 may be configured to facilitate charging the internal power source 229. As discussed before, a playback device comprising an internal power source may be referred to herein as a “portable playback device.” On the other hand, a playback device that operates using an external power source may be referred to herein as a “stationary playback device,” although such a device may in fact be moved around a home or other environment.
The playback device 102 further includes a user interface 240 that may facilitate user interactions independent of or in conjunction with user interactions facilitated by one or more of the controller devices 104. In various embodiments, the user interface 240 includes one or more physical buttons and/or supports graphical interfaces provided on touch sensitive screen(s) and/or surface(s), among other possibilities, for a user to directly provide input. The user interface 240 may further include one or more of lights (e.g., LEDs) and the speakers to provide visual and/or audio feedback to a user.
As an illustrative example,
As further shown in
By way of illustration, SONOS, Inc. presently offers (or has offered) for sale certain playback devices that may implement certain of the embodiments disclosed herein, including a “PLAY:1,” “PLAY:3,” “PLAY:5,” “PLAYBAR,” “CONNECT:AMP,” “PLAYBASE,” “BEAM,” “CONNECT,” and “SUB.” Any other past, present, and/or future playback devices may additionally or alternatively be used to implement the playback devices of example embodiments disclosed herein. Additionally, it should be understood that a playback device is not limited to the examples illustrated in
Based on certain command criteria, the NMD and/or a remote VAS may take actions as a result of identifying one or more commands in the voice input. Command criteria may be based on the inclusion of certain keywords within the voice input, among other possibilities. Additionally, or alternatively, command criteria for commands may involve identification of one or more control-state and/or zone-state variables in conjunction with identification of one or more particular commands. Control-state variables may include, for example, indicators identifying a level of volume, a queue associated with one or more devices, and playback state, such as whether devices are playing a queue, paused, etc. Zone-state variables may include, for example, indicators identifying which, if any, zone players are grouped.
In some implementations, the MPS 100 is configured to temporarily reduce the volume of audio content that it is playing upon detecting a certain keyword, such as a wake word, in the keyword portion 280a. The MPS 100 may restore the volume after processing the voice input 280. Such a process can be referred to as ducking, examples of which are disclosed in U.S. patent application Ser. No. 15/438,749, incorporated by reference herein in its entirety.
ASR for command keyword detection may be tuned to accommodate a wide range of keywords (e.g., 5, 10, 100, 1,000, 10,000 keywords). Command keyword detection, in contrast to wake-word detection, may involve feeding ASR output to an onboard, local NLU which together with the ASR determine when command word events have occurred. In some implementations described below, the local NLU may determine an intent based on one or more other keywords in the ASR output produced by a particular voice input. In these or other implementations, a playback device may act on a detected command keyword event only when the playback devices determines that certain conditions have been met, such as environmental conditions (e.g., low background noise).
b. Example Playback Device Configurations
For purposes of control, each zone in the MPS 100 may be represented as a single user interface (“UI”) entity. For example, as displayed by the controller devices 104, Zone A may be provided as a single entity named “Portable,” Zone B may be provided as a single entity named “Stereo,” and Zone C may be provided as a single entity named “Living Room.”
In various embodiments, a zone may take on the name of one of the playback devices belonging to the zone. For example, Zone C may take on the name of the Living Room device 102m (as shown). In another example, Zone C may instead take on the name of the Bookcase device 102d. In a further example, Zone C may take on a name that is some combination of the Bookcase device 102d and Living Room device 102m. The name that is chosen may be selected by a user via inputs at a controller device 104. In some embodiments, a zone may be given a name that is different than the device(s) belonging to the zone. For example, Zone B in
As noted above, playback devices that are bonded may have different playback responsibilities, such as playback responsibilities for certain audio channels. For example, as shown in
Additionally, playback devices that are configured to be bonded may have additional and/or different respective speaker drivers. As shown in
In some implementations, playback devices may also be “merged.” In contrast to certain bonded playback devices, playback devices that are merged may not have assigned playback responsibilities, but may each render the full range of audio content that each respective playback device is capable of. Nevertheless, merged devices may be represented as a single UI entity (i.e., a zone, as discussed above). For instance,
In some embodiments, a stand-alone NMD may be in a zone by itself. For example, the NMD 103h from
Zones of individual, bonded, and/or merged devices may be arranged to form a set of playback devices that playback audio in synchrony. Such a set of playback devices may be referred to as a “group,” “zone group,” “synchrony group,” or “playback group.” In response to inputs provided via a controller device 104, playback devices may be dynamically grouped and ungrouped to form new or different groups that synchronously play back audio content. For example, referring to
In various implementations, the zones in an environment may be assigned a particular name, which may be the default name of a zone within a zone group or a combination of the names of the zones within a zone group, such as “Dining Room+Kitchen,” as shown in
Referring back to
In some embodiments, the memory 213 of the playback device 102 may store instances of various variable types associated with the states. Variables instances may be stored with identifiers (e.g., tags) corresponding to type. For example, certain identifiers may be a first type “a1” to identify playback device(s) of a zone, a second type “b1” to identify playback device(s) that may be bonded in the zone, and a third type “c1” to identify a zone group to which the zone may belong. As a related example, in
In yet another example, the MPS 100 may include variables or identifiers representing other associations of zones and zone groups, such as identifiers associated with Areas, as shown in
The memory 213 may be further configured to store other data. Such data may pertain to audio sources accessible by the playback device 102 or a playback queue that the playback device (or some other playback device(s)) may be associated with. In embodiments described below, the memory 213 is configured to store a set of command data for selecting a particular VAS when processing voice inputs. During operation, one or more playback zones in the environment of
For instance, the user may be in the Office zone where the playback device 102n is playing the same hip-hop music that is being playing by playback device 102c in the Patio zone. In such a case, playback devices 102c and 102n may be playing the hip-hop in synchrony such that the user may seamlessly (or at least substantially seamlessly) enjoy the audio content that is being played out-loud while moving between different playback zones. Synchronization among playback zones may be achieved in a manner similar to that of synchronization among playback devices, as described in previously referenced U.S. Pat. No. 8,234,395.
As suggested above, the zone configurations of the MPS 100 may be dynamically modified. As such, the MPS 100 may support numerous configurations. For example, if a user physically moves one or more playback devices to or from a zone, the MPS 100 may be reconfigured to accommodate the change(s). For instance, if the user physically moves the playback device 102c from the Patio zone to the Office zone, the Office zone may now include both the playback devices 102c and 102n. In some cases, the user may pair or group the moved playback device 102c with the Office zone and/or rename the players in the Office zone using, for example, one of the controller devices 104 and/or voice input. As another example, if one or more playback devices 102 are moved to a particular space in the home environment that is not already a playback zone, the moved playback device(s) may be renamed or associated with a playback zone for the particular space.
Further, different playback zones of the MPS 100 may be dynamically combined into zone groups or split up into individual playback zones. For example, the Dining Room zone and the Kitchen zone may be combined into a zone group for a dinner party such that playback devices 102i and 102l may render audio content in synchrony. As another example, bonded playback devices in the Den zone may be split into (i) a television zone and (ii) a separate listening zone. The television zone may include the Front playback device 102b. The listening zone may include the Right, Left, and SUB playback devices 102a, 102j, and 102k, which may be grouped, paired, or merged, as described above. Splitting the Den zone in such a manner may allow one user to listen to music in the listening zone in one area of the living room space, and another user to watch the television in another area of the living room space. In a related example, a user may utilize either of the NMD 103a or 103b (
c. Example Controller Devices
The memory 413 of the controller device 104 may be configured to store controller application software and other data associated with the MPS 100 and/or a user of the system 100. The memory 413 may be loaded with instructions in software 414 that are executable by the processor 412 to achieve certain functions, such as facilitating user access, control, and/or configuration of the MPS 100. The controller device 104 is configured to communicate with other network devices via the network interface 424, which may take the form of a wireless interface, as described above.
In one example, system information (e.g., such as a state variable) may be communicated between the controller device 104 and other devices via the network interface 424. For instance, the controller device 104 may receive playback zone and zone group configurations in the MPS 100 from a playback device, an NMD, or another network device. Likewise, the controller device 104 may transmit such system information to a playback device or another network device via the network interface 424. In some cases, the other network device may be another controller device.
The controller device 104 may also communicate playback device control commands, such as volume control and audio playback control, to a playback device via the network interface 424. As suggested above, changes to configurations of the MPS 100 may also be performed by a user using the controller device 104. The configuration changes may include adding/removing one or more playback devices to/from a zone, adding/removing one or more zones to/from a zone group, forming a bonded or merged player, separating one or more playback devices from a bonded or merged player, among others.
As shown in
The playback control region 542 (
The playback zone region 543 (
In some embodiments, the graphical representations of playback zones may be selectable to bring up additional selectable icons to manage or configure the playback zones in the MPS 100, such as a creation of bonded zones, creation of zone groups, separation of zone groups, and renaming of zone groups, among other possibilities.
For example, as shown, a “group” icon may be provided within each of the graphical representations of playback zones. The “group” icon provided within a graphical representation of a particular zone may be selectable to bring up options to select one or more other zones in the MPS 100 to be grouped with the particular zone. Once grouped, playback devices in the zones that have been grouped with the particular zone will be configured to play audio content in synchrony with the playback device(s) in the particular zone. Analogously, a “group” icon may be provided within a graphical representation of a zone group. In this case, the “group” icon may be selectable to bring up options to deselect one or more zones in the zone group to be removed from the zone group. Other interactions and implementations for grouping and ungrouping zones via a user interface are also possible. The representations of playback zones in the playback zone region 543 (
The playback status region 544 (
The playback queue region 546 may include graphical representations of audio content in a playback queue associated with the selected playback zone or zone group. In some embodiments, each playback zone or zone group may be associated with a playback queue comprising information corresponding to zero or more audio items for playback by the playback zone or zone group. For instance, each audio item in the playback queue may comprise a uniform resource identifier (URI), a uniform resource locator (URL), or some other identifier that may be used by a playback device in the playback zone or zone group to find and/or retrieve the audio item from a local audio content source or a networked audio content source, which may then be played back by the playback device.
In one example, a playlist may be added to a playback queue, in which case information corresponding to each audio item in the playlist may be added to the playback queue. In another example, audio items in a playback queue may be saved as a playlist. In a further example, a playback queue may be empty, or populated but “not in use” when the playback zone or zone group is playing continuously streamed audio content, such as Internet radio that may continue to play until otherwise stopped, rather than discrete audio items that have playback durations. In an alternative embodiment, a playback queue can include Internet radio and/or other streaming audio content items and be “in use” when the playback zone or zone group is playing those items. Other examples are also possible.
When playback zones or zone groups are “grouped” or “ungrouped,” playback queues associated with the affected playback zones or zone groups may be cleared or re-associated. For example, if a first playback zone including a first playback queue is grouped with a second playback zone including a second playback queue, the established zone group may have an associated playback queue that is initially empty, that contains audio items from the first playback queue (such as if the second playback zone was added to the first playback zone), that contains audio items from the second playback queue (such as if the first playback zone was added to the second playback zone), or a combination of audio items from both the first and second playback queues. Subsequently, if the established zone group is ungrouped, the resulting first playback zone may be re-associated with the previous first playback queue or may be associated with a new playback queue that is empty or contains audio items from the playback queue associated with the established zone group before the established zone group was ungrouped. Similarly, the resulting second playback zone may be re-associated with the previous second playback queue or may be associated with a new playback queue that is empty or contains audio items from the playback queue associated with the established zone group before the established zone group was ungrouped. Other examples are also possible.
With reference still to
The sources region 548 may include graphical representations of selectable audio content sources and/or selectable voice assistants associated with a corresponding VAS. The VASes may be selectively assigned. In some examples, multiple VASes, such as AMAZON's Alexa, MICROSOFT's Cortana, etc., may be invokable by the same NMD. In some embodiments, a user may assign a VAS exclusively to one or more NMDs. For example, a user may assign a first VAS to one or both of the NMDs 102a and 102b in the Living Room shown in
d. Example Audio Content Sources
The audio sources in the sources region 548 may be audio content sources from which audio content may be retrieved and played by the selected playback zone or zone group. One or more playback devices in a zone or zone group may be configured to retrieve for playback audio content (e.g., according to a corresponding URI or URL for the audio content) from a variety of available audio content sources. In one example, audio content may be retrieved by a playback device directly from a corresponding audio content source (e.g., via a line-in connection). In another example, audio content may be provided to a playback device over a network via one or more other playback devices or network devices. As described in greater detail below, in some embodiments audio content may be provided by one or more media content services.
Example audio content sources may include a memory of one or more playback devices in a media playback system such as the MPS 100 of
In some embodiments, audio content sources may be added or removed from a media playback system such as the MPS 100 of
At step 650b, the playback device 102 receives the message 651a and adds the selected media content to the playback queue for play back.
At step 650c, the control device 104 receives input corresponding to a command to play back the selected media content. In response to receiving the input corresponding to the command to play back the selected media content, the control device 104 transmits a message 651b to the playback device 102 causing the playback device 102 to play back the selected media content. In response to receiving the message 651b, the playback device 102 transmits a message 651c to the computing device 106 requesting the selected media content. The computing device 106, in response to receiving the message 651c, transmits a message 651d comprising data (e.g., audio data, video data, a URL, a URI) corresponding to the requested media content.
At step 650d, the playback device 102 receives the message 651d with the data corresponding to the requested media content and plays back the associated media content.
At step 650e, the playback device 102 optionally causes one or more other devices to play back the selected media content. In one example, the playback device 102 is one of a bonded zone of two or more players (
Referring to the
The NMD 703 further includes microphones 720 and the at least one network interface 720 as described above and may also include other components, such as audio amplifiers, a user interface, etc., which are not shown in
Each channel 762 may correspond to a particular microphone 720. For example, an NMD having six microphones may have six corresponding channels. Each channel of the detected sound SD may bear certain similarities to the other channels but may differ in certain regards, which may be due to the position of the given channel's corresponding microphone relative to the microphones of other channels. For example, one or more of the channels of the detected sound SD may have a greater signal to noise ratio (“SNR”) of speech to background noise than other channels.
As further shown in
The spatial processor 764 is typically configured to analyze the detected sound SD and identify certain characteristics, such as a sound's amplitude (e.g., decibel level), frequency spectrum, directionality, etc. In one respect, the spatial processor 764 may help filter or suppress ambient noise in the detected sound SD from potential user speech based on similarities and differences in the constituent channels 762 of the detected sound SD, as discussed above. As one possibility, the spatial processor 764 may monitor metrics that distinguish speech from other sounds. Such metrics can include, for example, energy within the speech band relative to background noise and entropy within the speech band—a measure of spectral structure—which is typically lower in speech than in most common background noise. In some implementations, the spatial processor 764 may be configured to determine a speech presence probability, examples of such functionality are disclosed in U.S. patent application Ser. No. 15/984,073, filed May 18, 2018, titled “Linear Filtering for Noise-Suppressed Speech Detection,” which is incorporated herein by reference in its entirety.
In operation, the one or more buffers 768—one or more of which may be part of or separate from the memory 213 (
The network interface 724 may then provide this information to a remote server that may be associated with the MPS 100. In one aspect, the information stored in the additional buffer 769 does not reveal the content of any speech but instead is indicative of certain unique features of the detected sound itself. In a related aspect, the information may be communicated between computing devices, such as the various computing devices of the MPS 100, without necessarily implicating privacy concerns. In practice, the MPS 100 can use this information to adapt and fine-tune voice processing algorithms, including sensitivity tuning as discussed below. In some implementations the additional buffer may comprise or include functionality similar to lookback buffers disclosed, for example, in U.S. patent application Ser. No. 15/989,715, filed May 25, 2018, titled “Determining and Adapting to Changes in Microphone Performance of Playback Devices”; U.S. patent application Ser. No. 16/141,875, filed Sep. 25, 2018, titled “Voice Detection Optimization Based on Selected Voice Assistant Service”; and U.S. patent application Ser. No. 16/138,111, filed Sep. 21, 2018, titled “Voice Detection Optimization Using Sound Metadata,” which are incorporated herein by reference in their entireties.
In any event, the detected-sound data forms a digital representation (i.e., sound-data stream), SDS, of the sound detected by the microphones 720. In practice, the sound-data stream SDS may take a variety of forms. As one possibility, the sound-data stream SDS may be composed of frames, each of which may include one or more sound samples. The frames may be streamed (i.e., read out) from the one or more buffers 768 for further processing by downstream components, such as the VAS wake-word engines 770 and the voice extractor 773 of the NMD 703.
In some implementations, at least one buffer 768 captures detected-sound data utilizing a sliding window approach in which a given amount (i.e., a given window) of the most recently captured detected-sound data is retained in the at least one buffer 768 while older detected-sound data is overwritten when it falls outside of the window. For example, at least one buffer 768 may temporarily retain 20 frames of a sound specimen at given time, discard the oldest frame after an expiration time, and then capture a new frame, which is added to the 19 prior frames of the sound specimen.
In practice, when the sound-data stream SDS is composed of frames, the frames may take a variety of forms having a variety of characteristics. As one possibility, the frames may take the form of audio frames that have a certain resolution (e.g., 16 bits of resolution), which may be based on a sampling rate (e.g., 44,100 Hz). Additionally, or alternatively, the frames may include information corresponding to a given sound specimen that the frames define, such as metadata that indicates frequency response, power input level, SNR, microphone channel identification, and/or other information of the given sound specimen, among other examples. Thus, in some embodiments, a frame may include a portion of sound (e.g., one or more samples of a given sound specimen) and metadata regarding the portion of sound. In other embodiments, a frame may only include a portion of sound (e.g., one or more samples of a given sound specimen) or metadata regarding a portion of sound.
In any case, downstream components of the NMD 703 may process the sound-data stream SDS For instance, the VAS wake-word engines 770 are configured to apply one or more identification algorithms to the sound-data stream SDS (e.g., streamed sound frames) to spot potential wake words in the detected-sound SD. This process may be referred to as automatic speech recognition. The VAS wake-word engine 770a and command keyword engine 771a apply different identification algorithms corresponding to their respective wake words, and further generate different events based on detecting a wake word in the detected-sound SD.
Example wake word detection algorithms accept audio as input and provide an indication of whether a wake word is present in the audio. Many first- and third-party wake word detection algorithms are known and commercially available. For instance, operators of a voice service may make their algorithm available for use in third-party devices. Alternatively, an algorithm may be trained to detect certain wake-words.
For instance, when the VAS wake-word engine 770a detects a potential VAS wake word, the VAS work-word engine 770a provides an indication of a “VAS wake-word event” (also referred to as a “VAS wake-word trigger”). In the illustrated example of
In multi-VAS implementations, the NMD 703 may include a VAS selector 774 (shown in dashed lines) that is generally configured to direct extraction by the voice extractor 773 and transmission of the sound-data stream SDS to the appropriate VAS when a given wake-word is identified by a particular wake-word engine (and a corresponding wake-word trigger), such as the VAS wake-word engine 770a and at least one additional VAS wake-word engine 770b (shown in dashed lines). In such implementations, the NMD 703 may include multiple, different VAS wake-word engines and/or voice extractors, each supported by a respective VAS.
Similar to the discussion above, each VAS wake-word engine 770 may be configured to receive as input the sound-data stream SDS from the one or more buffers 768 and apply identification algorithms to cause a wake-word trigger for the appropriate VAS. Thus, as one example, the VAS wake-word engine 770a may be configured to identify the wake word “Alexa” and cause the NMD 703a to invoke the AMAZON VAS when “Alexa” is spotted. As another example, the wake-word engine 770b may be configured to identify the wake word “Ok, Google” and cause the NMD 520 to invoke the GOOGLE VAS when “Ok, Google” is spotted. In single-VAS implementations, the VAS selector 774 may be omitted.
In response to the VAS wake-word event (e.g., in response to the signal SVW indicating the wake-word event), the voice extractor 773 is configured to receive and format (e.g., packetize) the sound-data stream SDS. For instance, the voice extractor 773 packetizes the frames of the sound-data stream SDS into messages. The voice extractor 773 transmits or streams these messages, MV, that may contain voice input in real time or near real time to a remote VAS via the network interface 724.
The VAS is configured to process the sound-data stream SDS contained in the messages MV sent from the NMD 703. More specifically, the NMD 703a is configured to identify a voice input 780 based on the sound-data stream SDS. As described in connection with
When a VAS wake-word event occurs, the VAS may first process the keyword portion within the sound-data stream SDS to verify the presence of a VAS wake word. In some instances, the VAS may determine that the keyword portion comprises a false wake word (e.g., the word “Election” when the word “Alexa” is the target VAS wake word). In such an occurrence, the VAS may send a response to the NMD 703a with an instruction for the NMD 703a to cease extraction of sound data, which causes the voice extractor 773 to cease further streaming of the detected-sound data to the VAS. The VAS wake-word engine 770a may resume or continue monitoring sound specimens until it spots another potential VAS wake word, leading to another VAS wake-word event. In some implementations, the VAS does not process or receive the keyword portion but instead processes only the utterance portion.
In any case, the VAS processes the utterance portion to identify the presence of any words in the detected-sound data and to determine an underlying intent from these words. The words may correspond to one or more commands, as well as certain keywords. The keyword may be, for example, a word in the voice input identifying a particular device or group in the MPS 100. For instance, in the illustrated example, the keyword may be one or more words identifying one or more zones in which the music is to be played, such as the Living Room and the Dining Room (
To determine the intent of the words, the VAS is typically in communication with one or more databases associated with the VAS (not shown) and/or one or more databases (not shown) of the MPS 100. Such databases may store various user data, analytics, catalogs, and other information for natural language processing and/or other processing. In some implementations, such databases may be updated for adaptive learning and feedback for a neural network based on voice-input processing. In some cases, the utterance portion may include additional information, such as detected pauses (e.g., periods of non-speech) between words spoken by a user, as shown in
After processing the voice input, the VAS may send a response to the MPS 100 with an instruction to perform one or more actions based on an intent it determined from the voice input. For example, based on the voice input, the VAS may direct the MPS 100 to initiate playback on one or more of the playback devices 102, control one or more of these playback devices 102 (e.g., raise/lower volume, group/ungroup devices, etc.), or turn on/off certain smart devices, among other actions. After receiving the response from the VAS, the wake-word engine 770a of the NMD 703 may resume or continue to monitor the sound-data stream SDS1 until it spots another potential wake-word, as discussed above.
In general, the one or more identification algorithms that a particular VAS wake-word engine, such as the VAS wake-word engine 770a, applies are configured to analyze certain characteristics of the detected sound stream SDS and compare those characteristics to corresponding characteristics of the particular VAS wake-word engine's one or more particular VAS wake words. For example, the wake-word engine 770a may apply one or more identification algorithms to spot spectral characteristics in the detected sound stream SDS that match the spectral characteristics of the engine's one or more wake words, and thereby determine that the detected sound SD comprises a voice input including a particular VAS wake word.
In some implementations, the one or more identification algorithms may be third-party identification algorithms (i.e., developed by a company other than the company that provides the NMD 703a). For instance, operators of a voice service (e.g., AMAZON) may make their respective algorithms (e.g., identification algorithms corresponding to AMAZON's ALEXA) available for use in third-party devices (e.g., the NMDs 103), which are then trained to identify one or more wake words for the particular voice assistant service. Additionally, or alternatively, the one or more identification algorithms may be first-party identification algorithms that are developed and trained to identify certain wake words that are not necessarily particular to a given voice service. Other possibilities also exist.
As noted above, the NMD 703a also includes a command keyword engine 771a in parallel with the VAS wake-word engine 770a. Like the VAS wake-word engine 770a, the command keyword engine 771a may apply one or more identification algorithms corresponding to one or more wake words. A “command keyword event” is generated when a particular command keyword is identified in the detected-sound SD. In contrast to the nonce words typically as utilized as VAS wake words, command keywords function as both the activation word and the command itself. For instance, example command keywords may correspond to playback commands (e.g., “play,” “pause,” “skip,” etc.) as well as control commands (“turn on”), among other examples. Under appropriate conditions, based on detecting one of these command keywords, the NMD 703a performs the corresponding command.
The command keyword engine 771a can employ an automatic speech recognizer 772. The ASR 772 is configured to output phonetic or phenomic representations, such as text corresponding to words, based on sound in the sound-data stream SDS to text. For instance, the ASR 772 may transcribe spoken words represented in the sound-data stream SDS to one or more strings representing the voice input 780 as text. The command keyword engine 771 can feed ASR output (labeled as SASR) to a local natural language unit (NLU) 779 that identifies particular keywords as being command keywords for invoking command-keyword events, as described below.
As noted above, in some example implementations, the NMD 703a is configured to perform natural language processing, which may be carried out using an onboard natural language processor, referred to herein as a natural language unit (NLU) 779. The local NLU 779 is configured to analyze text output of the ASR 772 of the command keyword engine 771a to spot (i.e., detect or identify) keywords in the voice input 780. In
In one aspect, the library of the local NLU 779 includes command keywords. When the local NLU 779 identifies a command keyword in the signal SASR, the command keyword engine 771a generates a command keyword event and performs a command corresponding to the command keyword in the signal SASR, assuming that one or more conditions corresponding to that command keyword are satisfied.
Further, the library of the local NLU 779 may also include keywords corresponding to parameters. The local NLU 779 may then determine an underlying intent from the matched keywords in the voice input 780. For instance, if the local NLU matches the keywords “David Bowie” and “kitchen” in combination with a play command, the local NLU 779 may determine an intent of playing David Bowie in the Kitchen 101h on the playback device 102i. In contrast to a processing of the voice input 780 by a cloud-based VAS, local processing of the voice input 780 by the local NLU 779 may be relatively less sophisticated, as the NLU 779 does not have access to the relatively greater processing capabilities and larger voice databases that a VAS generally has access to.
In some examples, the local NLU 779 may determine an intent with one or more slots, which correspond to respective keywords. For instance, referring back to the play David Bowie in the Kitchen example, when processing the voice input, the local NLU 779 may determine that an intent is to play music (e.g., intent=playMusic), while a first slot includes David Bowie as target content (e.g., slot1=DavidBowie) and a second slot includes the Kitchen 101h as the target playback device (e.g., slot2=kitchen). Here, the intent (to “playMusic”) is based on the command keyword and the slots are parameters modifying the intent to a particular target content and playback device.
Within examples, the command keyword engine 771a outputs a signal, SCW, that indicates the occurrence of a command keyword event to the local NLU 779. In response to the command keyword event (e.g., in response to the signal SCW indicating the command keyword event), the local NLU 779 is configured to receive and process the signal SASR. In particular, the local NLU 779 looks at the words within the signal SASR to find keywords that match keywords in the library of the local NLU 779.
Some error in performing local automatic speech recognition is expected. Within examples, the ASR 772 may generate a confidence score when transcribing spoken words to text, which indicates how closely the spoken words in the voice input 780 matches the sound patterns for that word. In some implementations, generating a command keyword event is based on the confidence score for a given command keyword. For instance, the command keyword engine 771a may generate a command keyword event when the confidence score for a given sound exceeds a given threshold value (e.g., 0.5 on a scale of 0-1, indicating that the given sound is more likely than not the command keyword). Conversely, when the confidence score for a given sound is at or below the given threshold value, the command keyword engine 771a does not generate the command keyword event.
Similarly, some error in performing keyword matching is expected. Within examples, the local NLU may generate a confidence score when determining an intent, which indicates how closely the transcribed words in the signal SASR match the corresponding keywords in the library of the local NLU. In some implementations, performing an operation according to a determined intent is based on the confidence score for keywords matched in the signal SASR. For instance, the NMD 703 may perform an operation according to a determined intent when the confidence score for a given sound exceeds a given threshold value (e.g., 0.5 on a scale of 0-1, indicating that the given sound is more likely than not the command keyword). Conversely, when the confidence score for a given intent is at or below the given threshold value, the NMD 703 does not perform the operation according to the determined intent.
As noted above, in some implementations, a phrase may be used a command keyword, which provides additional syllables to match (or not match). For instance, the phrase “play me some music” has more syllables than “play,” which provides additional sound patterns to match to words. Accordingly, command keywords that are phrases may generally be less prone to false wake words.
As indicated above, the NMD 703a generates a command keyword event (and performs a command corresponding to the detected command keyword) only when certain conditions corresponding to a detected command keyword are met. These conditions are intended to lower the prevalence of false positive command keyword events. For instance, after detecting the command keyword “skip,” the NMD 703a generates a command keyword event (and skips to the next track) only when certain playback conditions indicating that a skip should be performed are met. These playback conditions may include, for example, (i) a first condition that a media item is being played back, (ii) a second condition that a queue is active, and (iii) a third condition that the queue includes a media item subsequent to the media item being played back. If any of these conditions are not satisfied, the command keyword event is not generated (and no skip is performed).
The NMD 703a includes the one or more state machine(s) 775a to facilitate determining whether the appropriate conditions are met. The state machine 775a transitions between a first state and a second state based on whether one or more conditions corresponding to the detected command keyword are met. In particular, for a given command keyword corresponding to a particular command requiring one or more particular conditions, the state machine 775a transitions into a first state when one or more particular conditions are satisfied and transitions into a second state when at least one condition of the one or more particular conditions is not satisfied.
Within example implementations, the command conditions are based on states indicated in state variables. As noted above, the devices of the MPS 100 may store state variables describing the state of the respective device. For instance, the playback devices 102 may store state variables indicating the state of the playback devices 102, such as the audio content currently playing (or paused), the volume levels, network connection status, and the like). These state variables are updated (e.g., periodically, or based on an event (i.e., when a state in a state variable changes)) and the state variables further can be shared among the devices of the MPS 100, including the NMD 703.
Similarly, the NMD 703 may maintain these state variables (either by virtue of being implemented in a playback device or as a stand-alone NMD). The state machine 775a monitors the states indicated in these state variables, and determines whether the states indicated in the appropriate state variables indicate that the command condition(s) are satisfied. Based on these determinations, the state machine 775a transitions between the first state and the second state, as described above.
In some implementations, the command-keyword engine 771 may be disabled unless certain conditions have been met via the state machines. For example, the first state and the second state of the state machine 775a may operate as enable/disable toggles to the command keyword engine 771a. In particular, while a state machine 775a corresponding to a particular command keyword is in the first state, the state machine 775a enables the command keyword engine 771a of the particular command keyword. Conversely, while the state machine 775a corresponding to the particular command keyword is in the second state, the state machine 775a disables the command keyword engine 771a of the particular command keyword. Accordingly, the disabled command keyword engine 771a ceases analyzing the sound-data stream SDS. In such cases when at least one command condition is not satisfied, the NMD 703a may suppress generation of command keyword event when the command keyword engine 771a detects a command keyword. Suppressing generation may involve gating, blocking or otherwise preventing output from the command keyword engine 771a from generating the command keyword event. Alternatively, suppressing generation may involve the NMD 703 ceasing to feed the sound-data stream SDS to the ASR 772. Such suppression prevents a command corresponding to the detected command keyword from being performed when at least one command condition is not satisfied. In such embodiments, the command keyword engine 771a may continue analyzing the sound-data stream SDS while the state machine 775a is in the first state, but command keyword events are disabled.
Other example conditions may be based on the output of a voice activity detector (“VAD”) 765. The VAD 765 is configured to detect the presence (or lack thereof) of voice activity in the sound-data stream SDS. In particular, the VAD 765 may analyze frames corresponding to the pre-roll portion of the voice input 780 (
The VAD 765 may utilize any suitable voice activity detection algorithms. Example voice detection algorithms involve determining whether a given frame includes one or more features or qualities that correspond to voice activity, and further determining whether those features or qualities diverge from noise to a given extent (e.g., if a value exceeds a threshold for a given frame). Some example voice detection algorithms involve filtering or otherwise reducing noise in the frames prior to identifying the features or qualities.
In some examples, the VAD 765 may determine whether voice activity is present in the environment based on one or more metrics. For example, the VAD 765 can be configured distinguish between frames that include voice activity and frames that don't include voice activity. The frames that the VAD determines have voice activity may be caused by speech regardless of whether it near- or far-field. In this example and others, the VAD 765 may determine a count of frames in the pre-roll portion of the voice input 780 that indicate voice activity. If this count exceeds a threshold percentage or number of frames, the VAD 765 may be configured to output a signal or set a state variable indicating that voice activity is present in the environment. Other metrics may be used as well in addition to, or as an alternative to, such a count.
The presence of voice activity in an environment may indicate that a voice input is being directed to the NMD 73. Accordingly, when the VAD 765 indicates that voice activity is not present in the environment (perhaps as indicated by a state variable set by the VAD 765) this may be configured as one of the command conditions for the command keywords. When this condition is met (i.e., the VAD 765 indicates that voice activity is present in the environment), the state machine 775a will transition to the first state to enable performing commands based on command keywords, so long as any other conditions for a particular command keyword are satisfied.
Further, in some implementations, the NMD 703 may include a noise classifier 766. The noise classifier 766 is configured to determine sound metadata (frequency response, signal levels, etc.) and identify signatures in the sound metadata corresponding to various noise sources. The noise classifier 766 may include a neural network or other mathematical model configured to identify different types of noise in detected sound data or metadata. One classification of noise may be speech (e.g., far-field speech). Another classification, may be a specific type of speech, such as background speech, and example of which is described in greater detail with reference to
For example, analyzing the sound metadata can include comparing one or more features of the sound metadata with known noise reference values or a sample population data with known noise. For example, any features of the sound metadata such as signal levels, frequency response spectra, etc. can be compared with noise reference values or values collected and averaged over a sample population. In some examples, analyzing the sound metadata includes projecting the frequency response spectrum onto an eigenspace corresponding to aggregated frequency response spectra from a population of NMDs. Further, projecting the frequency response spectrum onto an eigenspace can be performed as a pre-processing step to facilitate downstream classification.
In various embodiments, any number of different techniques for classification of noise using the sound metadata can be used, for example machine learning using decision trees, or Bayesian classifiers, neural networks, or any other classification techniques. Alternatively or additionally, various clustering techniques may be used, for example K-Means clustering, mean-shift clustering, expectation-maximization clustering, or any other suitable clustering technique. Techniques to classify noise may include one or more techniques disclosed in U.S. application Ser. No. 16/227,308 filed Dec. 20, 2018, and titled “Optimization of Network Microphone Devices Using Noise Classification,” which is herein incorporated by reference in its entirety.
To illustrate,
Referring back to
As noted above, one classification of sound may be background speech, such as speech indicative of far-field speech and/or speech indicative of a conversation not involving the NMD 703. The noise classifier 766 may output a signal and/or set a state variable indicating that background speech is present in the environment. The presence of voice activity (i.e., speech) in the pre-roll portion of the voice input 780 indicates that the voice input 780 might not be directed to the NMD 703, but instead be conversational speech within the environment. For instance, a household member might speak something like “our kids should have a play date soon” without intending to direct the command keyword “play” to the NMD 703.
Further, when the noise classifier indicates that background speech is present is present in the environment, this condition may disable the command keyword engine 771a. In some implementations, the condition of background speech being absent in the environment (perhaps as indicated by a state variable set by the noise classifier 766) is configured as one of the command conditions for the command keywords. Accordingly, the state machine 775a will not transition to the first state when the noise classifier 766 indicates that background speech is present in the environment.
Further, the noise classifier 766 may determine whether background speech is present in the environment based on one or more metrics. For example, the noise classifier 766 may determine a count of frames in the pre-roll portion of the voice input 780 that indicate background speech. If this count exceeds a threshold percentage or number of frames, the noise classifier 766 may be configured to output the signal or set the state variable indicating that background speech is present in the environment. Other metrics may be used as well in addition to, or as an alternative to, such a count.
Within example implementations, the NMD 703a may support a plurality of command keywords. To facilitate such support, the command keyword engine 771a may implement multiple identification algorithms corresponding to respective command keywords. Alternatively, the NMD 703a may implement additional command keyword engines 771b configured to identify respective command keywords. Yet further, the library of the local NLU 779 may include a plurality of command keywords and be configured to search for text patterns corresponding to these command keywords in the signal SASR.
Further, command keywords may require different conditions. For instance, the conditions for “skip” may be different than the conditions for “play” as “skip” may require that the condition that a media item is being played back and play may require the opposite condition that a media item is not being played back. To facilitate these respective conditions, the NMD 703a may implement respective state machines 775a corresponding to each command keyword. Alternatively, the NMD 703a may implement a state machine 775a having respective states for each command keyword. Other examples are possible as well.
In some example implementations, the VAS wake-word engine 770a generates a VAS wake-word event when certain conditions are met. The NMD 703b includes a state machine 775b, which is similar to the state machine 775a. The state machine 775b transitions between a first state and a second state based on whether one or more conditions corresponding to the VAS wake word are met.
For instance, in some examples, the VAS wake-word engine 770a may generate a VAS wake word event only when background speech was not present in the environment before a VAS wake-word event was detected. An indication of whether voice activity is present in the environment may come from the noise classifier 766. As noted above, the noise classifier 766 may be configured to output a signal or set a state variable indicating that far-field speech is present in the environment. Yet further, the VAS wake-word engine 770a may generate a VAS wake word event only when voice activity is present in the environment. As indicated above, the VAD 765 may be configured to output a signal or set a state variable indicating that voice activity is present in the environment.
To illustrate, as shown in
Yet further, the NMD 703 may include one or more sensors that output a signal indicating whether one or more users are in proximity to the NMD 703. Example sensors include a temperature sensor, an infrared sensor, an imaging sensor, and/or a capacitive sensor, among other examples. The NMD 703 may use output from such sensors to set one or more state variables indicating whether one or more users are in proximity to the NMD 703. Then, the state machine 775b may use the presence or lack thereof as a condition for the state machine 775b. For instance, the state machine 775b may enable the VAS wake-word engine and/or the command keyword engine 771a when at least one user is in proximity to the NMD 703.
To illustrate exemplary state machine operation,
At 777b, the state machine 775 transitions into the second state 778b when any command condition is not satisfied. At 777c, the state machine 775 remains in the second state 778b while any command condition is not satisfied. While the state machine 775 remains in the second state 778b, the NMD 703a will not act on the command keyword event when the command keyword is detected by the command keyword engine 771a.
Referring back to
For instance, the NMD 703a may include a particular streaming audio service (e.g., Apple Music) command keyword engine 771b. This particular command keyword engine 771b may be configured to detect command keywords specific to the particular streaming audio service and generate streaming audio service wake word events. For instance, one command keyword may be “Friends Mix,” which corresponds to a command to play back a custom playlists generated from playback histories of one or more “friends” within the particular streaming audio service.
A custom command keyword engine 771b may be relatively more prone to false wake words than the VAS wake-word engine 770a, as generally the VAS wake-word engine 770a is more sophisticated than a custom command keyword engine 771b. To mitigate this, custom command keywords may require one or more conditions to be satisfied before generating a custom command keyword event. Further, in some implementations, in an effort to reduce the prevalence of false positives, multiple conditions may be imposed as a requirement to include a custom command keyword engine 771b in the NMD 703a.
These custom command keyword conditions may include service-specific conditions. For instance, command keywords corresponding to premium features or playlists may require a subscription as a condition. As another example, custom command keywords corresponding to a particular streaming audio service may require media items from that streaming audio service in the playback queue. Other conditions are possible as well.
To gate custom command keyword engines based on the custom command keyword conditions, the NMD 703a may additional state machines 775a corresponding to each custom command keyword. Alternatively, the NMD 703a may implement a state machine 775a having respective states for each custom command keyword. Other examples are possible as well. These custom command conditions may depend on the state variables maintained by the devices within the MPS 100, and may also depend on state variables or other data structures representing a state of a user account of a cloud service, such as a streaming audio service.
Referring back to
To further reduce false positives, the command keyword engine 771a may utilize a relative low sensitivity compared with the VAS wake-word engine 770a. In practice, a wake-word engine may include a sensitivity level setting that is modifiable. The sensitivity level may define a degree of similarity between a word identified in the detected sound stream SDS1 and the wake-word engine's one or more particular wake words that is considered to be a match (i.e., that triggers a VAS wake-word or command keyword event). In other words, the sensitivity level defines how closely, as one example, the spectral characteristics in the detected sound stream SDS2 must match the spectral characteristics of the engine's one or more wake words to be a wake-word trigger.
In this respect, the sensitivity level generally controls how many false positives that the VAS wake-word engine 770a and command keyword engine 771a identifies. For example, if the VAS wake-word engine 770a is configured to identify the wake-word “Alexa” with a relatively high sensitivity, then false wake words of “Election” or “Lexus” may cause the wake-word engine 770a to flag the presence of the wake-word “Alexa.” In contrast, if the command keyword engine 771a is configured with a relatively low sensitivity, then the false wake words of “may” or “day” would not cause the command keyword engine 771a to flag the presence of the command keyword “Play.”
In practice, a sensitivity level may take a variety of forms. In example implementations, a sensitivity level takes the form of a confidence threshold that defines a minimum confidence (i.e., probability) level for a wake-word engine that serves as a dividing line between triggering or not triggering a wake-word event when the wake-word engine is analyzing detected sound for its particular wake word. In this regard, a higher sensitivity level corresponds to a lower confidence threshold (and more false positives), whereas a lower sensitivity level corresponds to a higher confidence threshold (and fewer false positives). For example, lowering a wake-word engine's confidence threshold configures it to trigger a wake-word event when it identifies words that have a lower likelihood that they are the actual particular wake word, whereas raising the confidence threshold configures the engine to trigger a wake-word event when it identifies words that have a higher likelihood that they are the actual particular wake word. Within examples, a sensitivity level of the command keyword engine 771a may be based on more or more confidence scores, such as the confidence score in spotting a command keyword and/or a confidence score in determining an intent. Other examples of sensitivity levels are also possible.
In example implementations, sensitivity level parameters (e.g., the range of sensitivities) for a particular wake-word engine can be updated, which may occur in a variety of manners. As one possibility, a VAS or other third-party provider of a given wake-word engine may provide to the NMD 703 a wake-word engine update that modifies one or more sensitivity level parameters for the given VAS wake-word engine 770a. By contrast, the sensitive level parameters of the command keyword engine 771a may be configured by the manufacturer of the NMD 703a or by another cloud service (e.g., for a custom wake-word engine 771b).
Notably, within certain examples, the NMD 703a foregoes sending any data representing the detected sound SD (e.g., the messages MV) to a VAS when processing a voice input 780 including a command keyword. In implementations including the local NLU 779, the NMD 703a can further process the voice utterance portion of the voice input 780 (in addition to the keyword word portion) without necessarily sending the voice utterance portion of the voice input 780 to the VAS. Accordingly, speaking a voice input 780 (with a command keyword) to the NMD 703 may provide increased privacy relative to other NMDs that process all voice inputs using a VAS.
As indicated above, the keywords in the library of the local NLU 779 correspond to parameters. These parameters may define to perform the command corresponding to the detected command keyword. When keywords are recognized in the voice input 780, the command corresponding to the detected command keyword is performed according to parameters corresponding to the detected keywords.
For instance, an example voice input 780 may be “play music at low volume” with “play” being the command keyword portion (corresponding to a playback command) and “music at low volume” being the voice utterance portion. When analyzing this voice input 780, the NLU 779 may recognize that “low volume” is a keyword in its library corresponding to a parameter representing a certain (low) volume level. Accordingly, the NLU 779 may determine an intent to play at this lower volume level. Then, when performing the playback command corresponding to “play,” this command is performed according to the parameter representing a certain volume level.
In a second example, another example voice input 780 may be “play my favorites in the Kitchen” with “play” again being the command keyword portion (corresponding to a playback command) and “my favorites in the Kitchen” as the voice utterance portion. When analyzing this voice input 780, the NLU 779 may recognize that “favorites” and “Kitchen” match keywords in its library. In particular, “favorites” corresponds to a first parameter representing particular audio content (i.e., a particular playlist that includes a user's favorite audio tracks) while “Kitchen” corresponds to a second parameter representing a target for the playback command (i.e., the kitchen 101h zone. Accordingly, the NLU 779 may determine an intent to play this particular playlist in the kitchen 101h zone.
In a third example, a further example voice input 780 may be “volume up” with “volume” being the command keyword portion (corresponding to a volume adjustment command) and “up” being the voice utterance portion. When analyzing this voice input 780, the NLU 779 may recognize that “up” is a keyword in its library corresponding to a parameter representing a certain volume increase (e.g., a 10 point increase on a 100 point volume scale). Accordingly, the NLU 779 may determine an intent to increase volume. Then, when performing the volume adjustment command corresponding to “volume,” this command is performed according to the parameter representing the certain volume increase.
Within examples, certain command keywords are functionally linked to a subset of the keywords within the library of the local NLU 779, which may hasten analysis. For instance, the command keyword “skip” may be functionality linked to the keywords “forward” and “backward” and their cognates. Accordingly, when the command keyword “skip” is detected in a given voice input 780, analyzing the voice utterance portion of that voice input 780 with the local NLU 779 may involve determining whether the voice input 780 includes any keywords that match these functionally linked keywords (rather than determining whether the voice input 780 includes any keywords that match any keyword in the library of the local NLU 779). Since vastly fewer keywords are checked, this analysis is relatively quicker than a full search of the library. By contrast, a nonce VAS wake word such as “Alexa” provides no indication as to the scope of the accompanying voice input.
Some commands may require one or more parameters, as such the command keyword alone does not provide enough information to perform the corresponding command. For example, the command keyword “volume” might require a parameter to specify a volume increase or decrease, as the intent of “volume” of volume alone is unclear. As another example, the command keyword “group” may require two or more parameters identifying the target devices to group.
Accordingly, in some example implementations, when a given command keyword is detected in the voice input 780 by the command keyword engine 771a, the local NLU 779 may determine whether the voice input 780 includes keywords matching keywords in the library corresponding to the required parameters. If the voice input 780 does include keywords matching the required parameters, the NMD 703a proceeds to perform the command (corresponding to the given command keyword) according to the parameters specified by the keywords.
However, if the voice input 780 does include keywords matching the required parameters for the command, the NMD 703a may prompt the user to provide the parameters. For instance, in a first example, the NMD 703a may play an audible prompt such as “I've heard a command, but I need more information” or “Can I help you with something?” Alternatively, the NMD 703a may send a prompt to a user's personal device via a control application (e.g., the software components 132c of the control device(s) 104).
In further examples, the NMD 703a may play an audible prompt customized to the detected command keyword. For instance, after detect a command keyword corresponding to a volume adjustment command (e.g., “volume”), the audible prompt may include a more specific request such as “Do you want to adjust the volume up or down?” As another example, for a grouping command corresponding to the command keyword “group,” the audible prompt may be “Which devices do you want to group?” Supporting such specific audible prompts may be made practicable by supporting a relatively limited number of command keywords (e.g., less than 100), but other implementations may support more command keywords with the trade-off of requiring additional memory and processing capability.
Within additional examples, when a voice utterance portion does not include keywords corresponding to one or more required parameters, the NMD 703a may perform the corresponding command according to one or more default parameters. For instance, if a playback command does not include keywords indicating target playback devices 102 for playback, the NMD 703a may default to playback on the NMD 703a itself (e.g., if the NMD 703a is implemented within a playback device 102) or to playback on one or more associated playback devices 102 (e.g., playback devices 102 in the same room or zone as the NMD 703a). Further, in some examples, the user may configure default parameters using a graphical user interface (e.g., user interface 430) or voice user interface. For example, if a grouping command does not specify the playback devices 102 to group, the NMD 703a may default to instructing two or more pre-configured default playback devices 102 to form a synchrony group. Default parameters may be stored in data storage (e.g., the memory 112b (
In some cases, the NMD 703a sends the voice input 780 to a VAS when the local NLU 779 is unable to process the voice input 780 (e.g., when the local NLU is unable to find matches to keywords in the library, or when the local NLU 779 has a low confidence score as to intent). In an example, to trigger sending the voice input 780, the NMD 703a may generate a bridging event, which causes the voice extractor 773 to process the sound-data stream SD, as discussed above. That is, the NMD 703a generates a bridging event to trigger the voice extractor 773 without a VAS wake-word being detected by the VAS wake-word engine 770a (instead based on a command keyword in the voice input 780, as well as the NLU 779 being unable to process the voice input 780).
Before sending the voice input 780 to the VAS (e.g., via the messages MV), the NMD 703a may obtain confirmation from the user that the user acquiesces to the voice input 780 being sent to the VAS. For instance, the NMD 703a may play an audible prompt to send the voice input to a default or otherwise configured VAS, such as “I'm sorry, I didn't understand that. May I ask Alexa?” In another example, the NMD 703a may play an audible prompt using a VAS voice (i.e., a voice that is known to most users as being associated with a particular VAS), such as “Can I help you with something?” In such examples, generation of the bridging event (and trigging of the voice extractor 773) is contingent on a second affirmative voice input 780 from the user.
Within certain example implementations, the local NLU 779 may process the signal SASR without necessarily a command keyword event being generated by the command keyword engine 771a (i.e., directly). That is, the automatic speech recognition 772 may be configured to perform automatic speech recognition on the sound-data stream SD, which the local NLU 779 processes for matching keywords without requiring a command keyword event. If keywords in the voice input 780 are found to match keywords corresponding to a command (possibly with one or more keywords corresponding to one or more parameters), the NMD 703a performs the command according to the one or more parameters.
Further, in such examples, the local NLU 779 may process the signal SASR directly only when certain conditions are met. In particular, in some embodiments, the local NLU 779 processes the signal SASR only when the state machine 775a is in the first state. The certain conditions may include a condition corresponding to no background speech in the environment. An indication of whether background speech is present in the environment may come from the noise classifier 766. As noted above, the noise classifier 766 may be configured to output a signal or set a state variable indicating that far-field speech is present in the environment. Further, another condition may corresponding to voice activity in the environment. The VAD 765 may be configured to output a signal or set a state variable indicating that voice activity is present in the environment. Similarly, The prevalence of false positive detection of commands with a direct processing approach may be mitigated using the conditions determined by the state machine 775a.
In some examples, the library of the local NLU 779 is partially customized to the individual user(s). In a first aspect, the library may be customized to the devices that are within the household of the NMD (e.g., the household within the environment 101 (
Within example implementations, the NMD 703a may populate the library of the local NLU 779 locally within the network 111 (
In further examples, the NMD 703a may populate the library by discovering devices connected to the network 111. For instance, the NMD 703a may transmit discovery requests via the network 111 according to a protocol configured for device discovery, such as universal plug-and-play (UPnP) or zero-configuration networking. Devices on the network 111 may then respond to the discovery requests and exchange data representing the device names, identifiers, addresses and the like to facilitate communication and control via the network 111. The NMD 703a may read these names from the exchanged messages and include them in the library of the local NLU 779 by training the local NLU 779 to recognize them as keywords.
In further examples, the NMD 703a may populate the library using the cloud. To illustrate,
One or more communication links 903a, 903b, and 903c (referred to hereinafter as “the links 903”) communicatively couple the MPS 100 and the cloud servers 906. The links 903 can include one or more wired networks and one or more wireless networks (e.g., the Internet). Further, similar to the network 111 (
In some implementations, the media playback system control servers 906a facilitate populating the library of local NLU 779 with the NMD(s) 703a (representing one or more of the NMD 703a (
In some examples, the media playback system control servers 906a may utilize user accounts and/or user profiles in obtaining keywords specific to the user. As noted above, a user of the MPS 100 may set-up a user profile to define settings and other information within the MPS 100. The user profile may then in turn be registered with user accounts of one or more streaming audio services to facilitate streaming audio from such services to the playback devices 102 of the MPS 100.
Through use of these registered streaming audio services, the streaming audio service servers 906b may collect data indicating a user's saved or preferred playlists, artists, albums, tracks, and the like, either via usage history or via user input (e.g., via a user input designating a media item as saved or a favorite). This data may be stored in a database on the streaming audio service servers 906b to facilitate providing certain features of the streaming audio service to the user, such as custom playlists, recommendations, and similar features. Under appropriate conditions (e.g., after receiving user permission), the streaming audio service servers 906b may share this data with the media playback system control servers 906a over the links 903b.
Accordingly, within examples, the media playback system control servers 906a may maintain or have access to data indicating a user's saved or preferred playlists, artists, albums, tracks, genres, and the like. If a user has registered their user profile with multiple streaming audio services, the saved data may include saved playlists, artists, albums, tracks, and the like from two or more streaming audio services. Further, the media playback system control servers 906a may develop a more complete understanding of the user's preferred playlists, artists, albums, tracks, and the like by aggregating data from the two or more streaming audio services, as compared with a streaming audio service that only has access to data generated through use of its own service.
Moreover, in some implementations, in addition to the data shared from the streaming audio service servers 906b, the media playback system control servers 906a may collect usage data from the MPS 100 over the links 903a, after receiving user permission. This may include data indicating a user's saved or preferred media items on a zone basis. Different types of music may be preferred in different rooms. For instance, a user may prefer upbeat music in the Kitchen 101h and more mellow music to assist with focus in the Office 101e.
Using the data indicating a user's saved or preferred playlists, artists, albums, tracks, and the like, the media playback system control servers 906a may identify names of playlists, artists, albums, tracks, and the like that the user is likely to refer to when providing playback commands to the NMDs 703a via voice input. Data representing these names can then be transmitted via the links 903a and the network 904 to the NMDs 703a and then added to the library of the local NLU 779 as keywords. For instance, the media playback system control servers 906a may send instructions to the NMDs 703a to include certain names as keywords in the library of the local NLU 779. Alternatively, the NMDs 703a (or another device of the MPS 100) may identify names of playlists, artists, albums, tracks, and the like that the user is likely to refer to when providing playback commands to the NMDs 703a via voice input and then include these names in the library of the local NLU 779.
Due to such customization, similar voice inputs may result in different operations being performed when the voice input is processed by the local NLU 779 as compared with processing by a VAS. For instance, a first voice input of “Alexa, play me my favorites in the Office” may trigger a VAS wake-word event, as it includes a VAS wake word (“Alexa”). A second voice input of “Play me my favorites in the Office” may trigger a command keyword, as it includes a command keyword (“play”). Accordingly, the first voice input is sent by the NMD 703a to the VAS, while the second voice input is processed by the local NLU 779.
While these voice inputs are nearly identical, they may cause different operations. In particular, the VAS may, to the best of its ability, determine a first playlist of audio tracks to add to a queue of the playback device 102f in the office 101e. Similarly, the local NLU 779 may recognize keywords “favorites” and “kitchen” in the second voice input. Accordingly, the NMD 703a performs the voice command of “play” with parameters of <favorites playlist> and <kitchen 101h zone>, which causes a second playlist of audio tracks to be added to the queue of the playback device 102f in the office 101e. However, the second playlist of audio tracks may include a more complete and/or more accurate collection of the user's favorite audio tracks, as the second playlist of audio tracks may draw on data indicating a user's saved or preferred playlists, artists, albums, and tracks from multiple streaming audio services, and/or the usage data collected by the media playback system control servers 906a. In contrast, the VAS may draw on its relatively limited conception of the user's saved or preferred playlists, artists, albums, and tracks when determining the first playlist.
To illustrate,
A household may include multiple users. Two or more users may configure their own respective user profiles with the MPS 100. Each user profile may have its own user accounts of one or more streaming audio services associated with the respective user profile. Further, the media playback system control servers 906a may maintain or have access to data indicating each user's saved or preferred playlists, artists, albums, tracks, genres, and the like, which may be associated with the user profile of that user.
In various examples, names corresponding to user profiles may be populated in the library of the local NLU 779. This may facilitate referring to a particular user's saved or preferred playlists, artists, albums, tracks, or genres. For instance, when a voice input of “Play Anne's favorites on the patio” is processed by the local NLU 779, the local NLU 779 may determine that “Anne” matches a stored keyword corresponding to a particular user. Then, when performing the playback command corresponding to that voice input, the NMD 703a adds a playlist of that particular user's favorite audio tracks to the queue of the playback device 102c in the patio 101i.
In some cases, a voice input might not include a keyword corresponding to a particular user, but multiple user profiles are configured with the MPS 100. In some cases, the NMD 703a may determine the user profile to use in performing a command using voice recognition. Alternatively, the NMD 703a may default to a certain user profile. Further, the NMD 703a may use preferences from the multiple user profiles when performing a command corresponding to a voice input that did not identify a particular user profile. For instance, the NMD 703a may determine a favorites playlist including preferred or saved audio tracks from each user profile registered with the MPS 100.
The IOT cloud servers 906c may be configured to provide supporting cloud services to the smart devices 990. The smart devices 990 may include various “smart” internet-connected devices, such as lights, thermostats, cameras, security systems, appliances, and the like. For instance, an IOT cloud server 906c may provide a cloud service supporting a smart thermostat, which allows a user to control the smart thermostat over the internet via a smartphone app or website.
Accordingly, within examples, the IOT cloud servers 906c may maintain or have access to data associated with a user's smart devices 990, such as device names, settings, and configuration. Under appropriate conditions (e.g., after receiving user permission), the IOT cloud servers 906c may share this data with the media playback system control servers 906a and/or the NMD 703a via the links 903c. For instance, the IOT cloud servers 906c that provide the smart thermostat cloud service may provide data representing such keywords to the NMD 703a, which facilitates populating the library of the local NLU 779 with keywords corresponding to the temperature.
Yet further, in some cases, the IOT cloud servers 906c may also provide keywords specific to control of their corresponding smart devices 990. For instance, the IOT cloud server 906c that provides the cloud service supporting the smart thermostat may provide a set of keywords corresponding to voice control of a thermostat, such as “temperature,” “warmer,” or “cooler,” among other examples. Data representing such keywords may be sent to the NMDs 703a over the links 903 and the network 904 from the IOT cloud servers 906c.
As noted above, some households may include more than NMD 703a. In example implementations, two or more NMDs 703a may synchronize or otherwise update the libraries of their respective local NLU 779. For instance, a first NMD 703a and a second NMD 703a may share data representing the libraries of their respective local NLU 779, possibly using a network (e.g., the network 904). Such sharing may facilitate the NMDs 703a being able to respond to voice input similarly, among other possible benefits.
In some embodiments, one or more of the components described above can operate in conjunction with the microphones 720 to detect and store a user's voice profile, which may be associated with a user account of the MPS 100. In some embodiments, voice profiles may be stored as and/or compared to variables stored in a set of command information or data table. The voice profile may include aspects of the tone or frequency of a user's voice and/or other unique aspects of the user, such as those described in previously-referenced U.S. patent application Ser. No. 15/438,749.
In some embodiments, one or more of the components described above can operate in conjunction with the microphones 720 to determine the location of a user in the home environment and/or relative to a location of one or more of the NMDs 103. Techniques for determining the location or proximity of a user may include one or more techniques disclosed in previously-referenced U.S. patent application Ser. No. 15/438,749, U.S. Pat. No. 9,084,058 filed Dec. 29, 2011, and titled “Sound Field Calibration Using Listener Localization,” and U.S. Pat. No. 8,965,033 filed Aug. 31, 2012, and titled “Acoustic Optimization.” Each of these applications is herein incorporated by reference in its entirety.
Examples described herein involve techniques to trigger voice assistant(s) on a network microphone device (NMD), such as one of the NMDs 103 (which may include features as described in connection with the NMD 703a (
A user may utilize different techniques to trigger capture of a voice input based on their distance from one of the NMDs 103. Some of these techniques are “wake-wordless” in that they involve triggering a voice assistant without the user having to speak an explicit wake-word. Instead, the NMD 103 may trigger the voice assistant based on two or more factors, such as one or more physical conditions and the presence of voice activity.
In some examples, these physical conditions may relate to proximity of a use within different distances or ranges. Such user presence conditions may be referred to as proximity triggers. A NMD 103 may enable a wakewordless mode when one of these conditions are detected. In the wakewordless mode, NMD may monitor for voice inputs without necessarily requiring a wake word, such as (Hey Alexa® or OK Google®). Instead, the NMD 103 monitors for keywords. A portion of the keywords may correspond to respective functions, such as playback functions (e.g., play, pause, skip, and the like), as described in connection with the foregoing sections.
In further examples, in the wakewordless mode, the NMD 103 may lower confidence thresholds for considering a detected sound to be a voice input. Within examples, the confidence thresholds may be lowered since the proximity triggers for entering the wakewordless mode indicate conditions where a user is more likely to be interacting with the NMDs 103, which increases the overall confidence that a sound detected while in the wakewordless mode is a voice input (and not other speech). For instance, the NMD 103 may lower the confidence threshold from 0.7 to 0.4. Other values are possible as well.
Within a first range 1210a (e.g., less than 1 meter), voice assistant(s) on the NMD 103a may be triggered using a combination of voice and touch. In an example, a housing of the NMD 103a (or a portion thereof, e.g., more than 50%) is touch sensitive (e.g., capacitive). The NMD 103a may be configured to interpret user gestures and other motions that come into contact or close proximity with the housing of the NMD 103a as an intent to address the voice assistant. As such, the NMD 103a may start listening for voice activity and/or lower one or more thresholds for interpreting voice activity as a voice input to the voice assistant(s) based on detecting such a gesture or motion via the housing of the NMD 103a. Notably, the housing of the NMD 103a may additionally or alternatively carry a button that, when pressed, explicitly triggers the voice assistant(s).
Within a second range 1220a (e.g., 1-5 meters), the voice assistant(s) on the NMD 103a may be triggered using line-of-sight or other types of presence detection. Within examples, line-of-sight or presence may be detected visually (e.g., via one or more cameras carried in the housing of the NMD 103a), which detect eye contact and/or other positioning of the user). In other examples, line-of-sight or presence may be detected aurally (e.g., via an analysis of the user's voice as captured by a microphone array carried in the housing of the NMD 103a to determine whether the user was facing in the direction of the NMD 103a when speaking). Similar to detection in the first range 1210a, the NMD 103a may start listening for voice activity and/or lower one or more thresholds for interpreting voice activity as a voice input to the voice assistant(s) based on detecting that the user is in line-of-sight to the NMD or present in proximity to the NMD 103a in a manner that is indicative of an intent to invoke a voice assistant.
Within a third range 1230a (e.g., far, or out of line-of-sight, such as 5+ meters), a user may trigger the voice assistant(s) on the NMD 103a using a wake word, which is a more conventional technique of triggering voice assistants. To interact with certain voice assistants, a user may speak a voice input that includes a wake word to trigger capture followed by an utterance comprising a user request. In practice, a wake word is typically a predetermined nonce word or phrase used to “wake up” an NMD and cause it to invoke a particular voice assistant service (“VAS”) to interpret the intent of voice input in detected sound. For example, a user might speak the wake word “Alexa” to invoke the AMAZON® VAS, “Ok, Google” to invoke the GOOGLE® VAS, “Hey, Siri” to invoke the APPLE® VAS, or “Hey, Sonos” to invoke a VAS offered by SONOS®, among other examples. A wake word may also be referred to as, for example, an activation-, trigger-, wakeup-word or -phrase, and may take the form of any suitable word, combination of words (e.g., a particular phrase), and/or some other audio cue.
In example implementations, the ranges may overlap. That is, within the first range 1210a, a user may have the option of trigger a voice assistant using touch, line-of-sight, or a wake word. Similarly, within the second range 1220a, a user may trigger a voice assistant using line-of-sight or a wake word (but be too far away to trigger the voice assistant using touch).
The first range 1210a, the second range 1220a, and the third range 1230a are not necessarily specific distances. Instead, these ranges may be inherent based on the capabilities of the sensors involved. For instance, when determining line-of-sight, implementation of more capable sensors and/or detection techniques in the NMD 103a may expand the second range 1220a. As another example, implementation of additional or more sensitive microphones may expand the second range 1220a when determining line-of-sight aurally or the third range 1230a when detecting a wake word.
The effective size of first range 1210a, the second range 1220a, and the third range 1230a may also be based on the users and/or the environment. For instance, the first range 1210a may be inherently larger for some users as compared with others, as the some users may have longer reach. Yet further, users that speak with a relatively loud clear voice may effectively expand the second range 1220a when determining line-of-sight aurally or the third range 1230a when detecting a wake word. As another example, when determining line-of-sight visually, the NMD 103a may be able to determine line-of-sight from a greater distance in a well-lit environment as compared with a dimly-lit environment, which may cause the second range 1220a to expand. Similarly, when determining line-of-sight aurally, the NMD 103a may be able to determine line-of-sight from a greater distance in a quiet environment as compared with a dimly-lit environment. Yet further, walls, furniture and other things within an environment may also affect the ranges.
To illustrate,
Within examples, two or more NMDs 103 may be grouped such that a detection by one of the associated NMDs may trigger the group, which may effectively expand the second and/or third ranges for a given NMD 103. Example groupings include bonded zones and zone groups (
To illustrate,
In an example, after detecting a user in line-of-sight, the NMD 103c may send an indication of such a detection to the NMD 102a. Based on receiving this indication, the NMD 103a may start listening for voice activity and/or lower one or more thresholds for interpreting voice activity as a voice input to the voice assistant(s) in a similar manner as if the NMD 103a detected the user in line of sight itself. As such, the second range 1220c is effectively based on the capabilities of the sensors in both the NMD 103a and the NMD 103c, as illustrated in
Yet further, in some examples, the NMDs 103 in a group may be configured to operate in the wakewordless mode at more or less the same time (i.e., concurrently). To facilitate such operation, the NMD 103a may send an indication or instruction to the NMD 103c to cause the NMD 103c to enable the wakewordless mode when the NMD 103a enables the wakewordless mode.
In further examples, when proximity triggers, such as a touch input or a user line-of-sight, are not detected, the NMDs 103 may disable the wakewordless mode. With the wakewordless mode disabled, the NMDs might not monitor for keywords, and instead monitor only for wake words. Yet further, the NMDs 103 may monitor for both keywords and wake words, with a higher confidence threshold requirement for detection of keywords.
Yet further, a voice activity detector (VAD) and a noise classifier have analyzed 150 frames of a pre-roll portion of the voice input. As shown, the VAD has detected voice in 140 frames of the 150 pre-roll frames, which indicates that a voice input may be present in the detected sound. Further, the noise classifier has detected ambient noise in 11 frames, background speech in 127 frames, and fan noise in 12 frames. In this NMD, the noise classifier is classifying the predominant noise source in each frame. This indicates the presence of background speech. As a result, the NMD has determined not to trigger on the detected command keyword “play.”
Yet further, a voice activity detector (VAD) and a noise classifier have analyzed 150 frames of a pre-roll portion of the voice input. As shown, the VAD has detected voice in 87 frames of the 150 pre-roll frames, which indicates that a voice input may be present in the detected sound. Further, the noise classifier has detected ambient noise in 18 frames, background speech in 8 frames, and fan noise in 124 frames. This indicates that background speech is not present. Given the foregoing, the NMD has determined to trigger on the detected command keyword “play.”
As shown, the ASR has transcribed the voice input as “play beet les in the kitchen.” Some error in performing ASR is expected (e.g., “beet les”). Here, the local NLU has matched the keyword “beet les” to “The Beatles” in the local NLU library, which sets up this artist as a content parameter to the play command. Further, the local NLU has also matched the keyword “kitchen” to “kitchen” in the local NLU library, which sets up the kitchen zone as a target parameter to the play command. The local NLU produced a confidence score of 0.63428231948273443 associated with the intent determination.
Here as well, a voice activity detector (VAD) and a noise classifier have analyzed 150 frames of a pre-roll portion of the voice input. As shown, the noise classifier has detected ambient noise in 142 frames, background speech in 8 frames, and fan noise in 0 frames. This indicates that background speech is not present. The VAD has detected voice in 112 frames of the 150 pre-roll frames, which indicates that a voice input may be present in the detected sound. Here, the NMD has determined to trigger on the detected command keyword “play.”
Yet further, a voice activity detector (VAD) and a noise classifier have analyzed 150 frames of a pre-roll portion of the voice input. As shown, the VAD has detected voice in 140 frames of the 150 pre-roll frames, which indicates that a voice input may be present in the detected sound. Further, the noise classifier has detected ambient noise in 11 frames, background speech in 127 frames, and fan noise in 12 frames This indicates the presence of background speech. As a result, the NMD has determined not to trigger on the detected command keyword “play.”
As shown, the ASR has transcribed the voice input as “lay some music in the office.” Here, the local NLU has matched the keyword “lay” to “play” in the local NLU library, which corresponds to a playback command. Further, the local NLU has also matched the keyword “office” to “office” in the local NLU library, which sets up the office zone as a target parameter to the play command. The local NLU produced a confidence score of 0.14620494842529297 associated with the keyword matching. In some examples, this low confidence score may cause the NMD to not accept the voice input (e.g., if this confidence score is below a threshold, such as 0.5).
At block 1402, the method 1400 involves monitoring for proximity triggers. Example proximity triggers correspond to conditions where a user is more likely to be interacting with an NMD. Within examples, the playback device 102f may monitor for proximity triggers into two or more ranges, perhaps using different sensors, as described in connection with
For instance, the playback device 102f may monitor for proximity triggers in a first range and a second range. That is, the playback device 102f may monitor for user proximity in a first range (e.g., the first range 1210a, the first range 1210b, or the first range 1210c, as shown in
Within examples, the playback device 102f may monitor for user proximity in the first range using at least one sensor. Example sensors include touch-sensitive sensor (e.g., a capacitive or resistive sensor), as well as other suitable sensors configured to detect a user in proximity to the NMD 103. In another example, the playback device 102f may monitor for a button press of a button on a housing of the playback device 102f, such as a button within the control area 232 on the housing 230 (
The NMD 103 may monitor for user line-of-sight using any suitable sensor. For instance, the NMD 103 may detect a user in line-of-sight to the playback device by detecting, via at least one microphone, audio data and determining that the audio data indicates that the user is speaking towards the playback device when speaking at least a portion of a voice input. In another example, the NMD 103 may detect the user in line-of-sight to the playback device by detecting, via at least one camera, image data and determining that the image data indicates that the user is looking towards the playback device when speaking at least a portion of a voice input.
At block 1404, the method 1400 involves enabling a wakewordless mode when a proximity trigger is detected. For instance, the playback device 102f may enable the wakewordless mode when at least one of (i) a touch input is detected via the at least one touch sensor or (ii) a user line-of-sight is detected, wherein the wakewordless mode is otherwise disabled. Other triggers are possible as well.
In the wakewordless mode, the playback device 102f may monitor for voice inputs without necessarily requiring a wake word, such as (Hey Alexa® or OK Google®). Instead, the playback device 102f monitors for keywords. A portion of the keywords may correspond to respective functions, such as playback functions (e.g., play, pause, skip, and the like).
In further examples, in the wakewordless mode, the playback device 102f may lower confidence thresholds for considering a detected sound to be a voice input. Within examples, the confidence thresholds may be lowered since the proximity triggers for entering the wakewordless mode indicate conditions where a user is more likely to be interacting with an NMD, which increases the overall confidence that a sound detected while in the wakewordless mode is a voice input (and not other speech). For instance, the playback device 102f may lower the confidence threshold from 0.5 to 0.25. Other values are possible as well.
At block 1406, the method 1400 involves monitoring a sound data stream for keywords while the wakewordless mode is enabled. For instance, as illustrated in
At block 1408, the method 1400 involves detecting a first voice input. For example, the playback device 102f may detect a first voice input in the monitored sound data stream SDS (
At block 1410, the method 1400 involves locally processing the first voice input. For instance, the playback device 102f may locally process the first voice input using the ASR 772 and/or the local NLU 779, among other suitable components (
Within examples, processing the first voice input locally on the playback device may involve performing a particular playback function corresponding to the one or more particular command keywords in the first voice input. For instance, if the one or more particular command keywords include a “play” keyword corresponding to a playback command, the playback device 102f may play audio content according to that playback command. As another example, if the one or more particular command keywords include a “turn on” keyword corresponding to a power on command, the playback device 102f may cause one or more IoT devices indicated in the first voice input to turn on. Other examples are possible as well.
In some examples, the playback device 102f may be in a synchrony group such as a bonded zone or a zone group with one or more additional playback devices 102 (
In such examples, the playback device 102f may play back the audio content in synchrony with the one or more additional playback devices 102 (e.g., any of the playback devices 102a-102o). In further examples, the first voice input may indicate one or more pre-saved groups (e.g., the second area illustrated in
At block 1412, the method 1400 involves monitoring a sound data stream for one or more wake words while the wakewordless mode is disabled. For instance, the playback device 102f may monitor for wakewords of one or more voice assistant services (e.g., Alexa®, Ok Google®, among other possible wake words). Within examples, as illustrated in
At block 1414, the method 1400 involves detecting a second voice input. For example, the playback device 102f may detect a second voice input that includes a wake word corresponding to a particular voice assistance service. In some examples, the playback device 102f may detect the second voice input via the microphones 720, the VCC 760, and/or the VAS keyword engine 770a, among other suitable components (
At block 1416, the method 1400 involves remotely processing the second voice input. For instance, the playback device 102f may remotely process the second voice input by streaming, via the network interface, data representing the second voice input to one or more remote servers of the particular voice assistant service for processing. Within examples, the playback device 102f may select the particular voice assistant service among multiple voice assistant services based on the particular wake word detected. In some examples, the method 1400 may locally process the voice input without invoking a remote voice server or remote voice assistant service, as discussed below.
In further examples, the playback device 102f may remotely process the second voice input by streaming, via the network interface, data representing the second voice input to one or more devices at the edge (e.g., on the LAN 111), which may have more voice input processing capability than the playback devices 102f. Other examples are possible as well. Examples of edge processing in a media playback system are described in U.S. Provisional Application No. 63/231,573 filed Aug. 10, 2021, and titled “Edge Data Caching in a Media Playback System,” which is herein incorporated by reference in its entirety.
In further examples, the plurality of command keywords may include a local wake word corresponding to local processing, which may or might not be the same as the wake word for the native voice assistant service (e.g., “Hey Sonos®” or “Hey Sonos® Local”). In such examples, the method 1400 may involve while the wakewordless mode is disabled, detecting, in the monitored sound data stream, a third voice input that includes the local wake word corresponding to local processing; and based on the third voice input including the local wake word, locally processing the third voice input.
In additional examples, the playback device 102f may be grouped with the playback device 102g and monitor for proximity triggers in concert with the playback device 102g, as described in connection with
In some implementations, the grouped playback devices 102 may be configured to enable or disable the wakewordless mode at the same time. To facilitate such operation, the playback device 102f may be configured to send an indication that the playback device 102f enabled the wakewordless mode to additional playback devices 102 in the group (e.g., the playback device 102g). Based on receiving this indication, the additional playback devices 102 in the group may enable the wakewordless mode themselves. Within examples, the playback device 102g may likewise notify the playback device 102f that the playback device 102g is enabling the wakewordless mode.
The description above discloses, among other things, various example systems, methods, apparatus, and articles of manufacture including, among other components, firmware and/or software executed on hardware. It is understood that such examples are merely illustrative and should not be considered as limiting. For example, it is contemplated that any or all of the firmware, hardware, and/or software aspects or components can be embodied exclusively in hardware, exclusively in software, exclusively in firmware, or in any combination of hardware, software, and/or firmware. Accordingly, the examples provided are not the only way(s) to implement such systems, methods, apparatus, and/or articles of manufacture.
The specification is presented largely in terms of illustrative environments, systems, procedures, steps, logic blocks, processing, and other symbolic representations that directly or indirectly resemble the operations of data processing devices coupled to networks. These process descriptions and representations are typically used by those skilled in the art to most effectively convey the substance of their work to others skilled in the art. Numerous specific details are set forth to provide a thorough understanding of the present disclosure. However, it is understood to those skilled in the art that certain embodiments of the present disclosure can be practiced without certain, specific details. In other instances, well known methods, procedures, components, and circuitry have not been described in detail to avoid unnecessarily obscuring aspects of the embodiments. Accordingly, the scope of the present disclosure is defined by the appended claims rather than the forgoing description of embodiments.
When any of the appended claims are read to cover a purely software and/or firmware implementation, at least one of the elements in at least one example is hereby expressly defined to include a tangible, non-transitory medium such as a memory, DVD, CD, Blu-ray, and so on, storing the software and/or firmware.
The present technology is illustrated, for example, according to various aspects described below. Various examples of aspects of the present technology are described as numbered examples (1, 2, 3, etc.) for convenience. These are provided as examples and do not limit the present technology. It is noted that any of the dependent examples may be combined in any combination, and placed into a respective independent example. The other examples can be presented in a similar manner.
Example 1: A method comprising: monitoring for (i) user proximity in a first range from the playback device via at least one touch-sensitive sensor and (ii) user line-of-sight in a second range that is further from the playback device than the first range; enabling a wakewordless mode when at least one of (i) a touch input is detected via the at least one touch sensor or (ii) a user line-of-sight is detected, wherein the wakewordless mode is otherwise disabled; while operating in the wakewordless mode: monitoring a sound data stream from at least one microphone for a plurality of command keywords corresponding to respective functions, wherein voice inputs including one or more of the plurality of command keywords are processable locally on the playback device; detecting a first voice input in the monitored sound data stream; and locally processing the first voice input, wherein local processing of voice input comprises determining that the detected first voice input includes one or more particular command keywords from among the plurality of command keywords corresponding to respective functions, wherein the first voice input excludes wake words corresponding to any voice assistant service; and while the wakewordless mode is disabled: monitoring, via the at least one microphone, the sound data stream for a wake word corresponding to a particular voice assistance service; detecting, in the monitored sound data stream, a second voice input that includes the wake word corresponding to the particular voice assistance service; and after detecting the second voice input that includes the wake word corresponding to the voice assistance service, processing the second voice input remotely via the particular voice assistant service.
Example 2: The method of Example 1, wherein processing the first voice input locally on the playback device comprises performing a particular playback function corresponding to the one or more particular command keywords.
Example 3: The method of Example 1 or 2, wherein processing the second voice input remotely via the particular voice assistant service comprises streaming, via the network interface, the second voice input to one or more remote servers of the particular voice assistant service for processing.
Example 4: The method of any preceding Example, wherein the plurality of command keywords comprises a local wake word corresponding to local processing, and wherein the method further comprises: while the wakewordless mode is disabled: detecting, in the monitored sound data stream, a third voice input that includes the local wake word corresponding to local processing; and based on the third voice input including the local wake word, locally processing the third voice input.
Example 5: The method of any preceding Example, wherein the playback device is grouped with an additional playback device, wherein the first voice input is a playback command, and wherein the method further comprises: after processing the first voice input locally on the playback device, playing back audio according to the first voice input in synchrony with playback of the audio by the additional playback device.
Example 6: The method of Example 5, further comprising: while the wakewordless mode is disabled, receiving, via the network interface, an indication that the additional playback device detected at least one of (i) a touch input via at least one touch sensor of the additional playback device or (ii) a user line-of-sight; and based on receiving the indication, enabling the wakewordless mode.
Example 7: The method of Example 5, further comprising: while the wakewordless mode is disabled, receiving, via the network interface, an indication that the additional playback device detected the wake word at a given time; and detecting, in the monitored sound data stream, a third voice input that includes the sound data stream at the given time.
Example 8: The method of Example 5, further comprising: after enabling the wakewordless mode, sending, via the network interface to the additional playback device, an indication that the playback device is operating in the wakewordless mode, wherein the additional playback device is configured to operate in the wakewordless mode after receiving the indication.
Example 9: The method of any preceding Example, wherein enabling the wakewordless mode comprises: detecting the user in line-of-sight to the playback device wherein detecting the user in line-of-sight to the playback device comprises: detecting, via the at least one microphone, audio data, and determining that the audio data indicates that the user is speaking towards the playback device when speaking at least a portion of the first voice input; and based on detecting the user in line-of-sight to the playback device, enabling the wakewordless mode.
Example 10: The method of any preceding Example, wherein the playback device comprises at least one camera, and wherein enabling the wakewordless mode comprises: detecting the user in line-of-sight to the playback device wherein detecting the user in line-of-sight to the playback device comprises: detecting, via the at least one camera, image data, and determining that the image data indicates that the user is looking towards the playback device when speaking at least a portion of the first voice input; and based on detecting the user in line-of-sight to the playback device, enabling the wakewordless mode.
Example 11: A tangible, non-transitory, computer-readable medium having instructions stored thereon that are executable by one or more processors to cause a playback device or an NMD to perform the method of any one of Examples 1-10.
Example 12: A device comprising a network interface, at least one microphone, one or more processors, and a tangible, non-tangible computer-readable medium having instructions stored thereon that are executable by the one or more processors to cause the device to perform the method of any one of Examples 1-10.
Example 13: A system comprising a playback device, a network interface, at least one microphone, one or more processors, and a tangible, non-tangible computer-readable medium having instructions stored thereon that are executable by the one or more processors to cause the system to perform the method of any one of Examples 1-10.
Example 14: A method comprising: monitoring for (i) user proximity in a first range from a first playback device via at least one touch-sensitive sensor, (ii) user line-of-sight in a second range that is further from the first playback device than the first range, and (iii) a particular indication that a second playback device detected a user line-of-sight to the second playback device; enabling a wakewordless mode when at least one of (i) a touch input is detected via the at least one touch sensor, (ii) a user line-of-sight is detected, or (iii) the particular indication is received, wherein the wakewordless mode is otherwise disabled; while operating in the wakewordless mode: monitoring a sound data stream from at least one microphone for a plurality of command keywords corresponding to respective functions, wherein voice inputs including one or more of the plurality of command keywords are processable locally on the playback device; detecting a first voice input in the monitored sound data stream; and locally processing the first voice input, wherein local processing of voice input comprises determining that the detected first voice input includes one or more particular command keywords from among the plurality of command keywords corresponding to respective functions, wherein the first voice input excludes wake words corresponding to any voice assistant service; and while the wakewordless mode is disabled: monitoring, via the at least one microphone, the sound data stream for a wake word corresponding to a particular voice assistance service; detecting, in the monitored sound data stream, a second voice input that includes the wake word corresponding to the particular voice assistance service; and after detecting the second voice input that includes the wake word corresponding to the voice assistance service, processing the second voice input remotely via the particular voice assistant service.
Example 15: The method of Example 14, wherein processing the first voice input locally on the first playback device comprises performing a particular playback function corresponding to the one or more particular command keywords.
Example 16: The method of any of Examples 14-15, wherein processing the second voice input remotely via the particular voice assistant service comprises streaming, via the network interface, the second voice input to one or more remote servers of the particular voice assistant service for processing.
Example 17: The method of Examples 14-16, wherein the plurality of command keywords comprises a local wake word corresponding to local processing, and wherein the method further comprises: while the wakewordless mode is disabled: detecting, in the monitored sound data stream, a third voice input that includes the local wake word corresponding to local processing; and based on the third voice input including the local wake word, locally processing the third voice input.
Example 18: The method of any of Examples 14-17, wherein the first playback device is in a synchrony group with the second playback device, wherein the first voice input is a playback command, and wherein the method further comprises: after processing the first voice input locally on the first playback device, playing back audio according to the first voice input in synchrony with playback of the audio by the second playback device.
Example 19: The method of any of Examples 14-18, further comprising: while the wakewordless mode is disabled, receiving, via the network interface, the particular indication that the second playback device detected the user line-of-sight to the second playback device; and based on receiving the indication, enabling the wakewordless mode.
Example 20: The method of any of Examples 14-19, further comprising: while the wakewordless mode is disabled, receiving, via the network interface, an indication that the second playback device detected the wake word at a given time; and detecting, in the monitored sound data stream, a third voice input that includes the sound data stream at the given time.
Example 21: The method of any of Examples 14-20, further comprising: after enabling the wakewordless mode, sending, via the network interface to the second playback device, an indication that the first playback device is operating in the wakewordless mode, wherein the second playback device is configured to operate in the wakewordless mode after receiving the indication.
Example 22: The method of any of Examples 14-20, further comprising: monitoring for (i) user proximity in a first range from the second playback device via the at least one additional touch-sensitive sensor, (ii) user line-of-sight in a second range that is further from the second playback device than the first range, and (iii) a particular indication that the first playback device detected a user line-of-sight to the first playback device; enabling the wakewordless mode on the second playback device when at least one of (i) a touch input is detected via the at least one additional touch sensor, (ii) a user line-of-sight is detected, or (iii) the particular indication that the first playback device detected the user line-of-sight to the first playback device is received, wherein the wakewordless mode is otherwise disabled; and after enabling the wakewordless mode on the second playback device, sending, via the additional network interface to the second playback device, the particular indication that the second playback device detected the user line-of-sight to the second playback device.
Example 23: A tangible, non-transitory, computer-readable medium having instructions stored thereon that are executable by one or more processors to cause an playback device to perform the method of any one of Examples 14-22.
Example 24: A playback device comprising one or more network interfaces, one or more processors, and a tangible, non-tangible computer-readable medium having instructions stored thereon that are executable by the one or more processors to cause the playback device to perform the method of any one of Examples 14-22.
Example 25: A system comprising a first playback device and a second playback device, one or more network interfaces, one or more processors, and a tangible, non-tangible computer-readable medium having instructions stored thereon that are executable by the one or more processors to cause the system to perform the method of any one of Examples 14-22.
This application claims the benefit under 35 U.S.C. § 119 of U.S. provisional App. No. 63/112,756 filed on Nov. 20, 2020, entitled “Network Device Interaction by Range,” which is incorporated herein by reference in its entirety.
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| 2016136062 | Sep 2016 | WO |
| 2016165067 | Oct 2016 | WO |
| 2016171956 | Oct 2016 | WO |
| 2016200593 | Dec 2016 | WO |
| 2017039632 | Mar 2017 | WO |
| 2017058654 | Apr 2017 | WO |
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| 2017147075 | Aug 2017 | WO |
| 2017147936 | Sep 2017 | WO |
| 2018027142 | Feb 2018 | WO |
| 2018067404 | Apr 2018 | WO |
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| 2019005772 | Jan 2019 | WO |
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| Number | Date | Country | |
|---|---|---|---|
| 20220148592 A1 | May 2022 | US |
| Number | Date | Country | |
|---|---|---|---|
| 63112756 | Nov 2020 | US |
