Patent Application
20230410810
This disclosure relates generally to audio analysis and, more particularly, to methods and apparatus to audio adjustment based on vocal effort.
During human communication, people can use different vocal efforts (e.g., depending on the context, types of conversations, etc.). Normally, speakers use a regular voice type, but different environmental or emotional stressing conditions can cause a person to change to another voice type (concern of being overheard, having a heated argument, too much background noise, etc.). Speakers using soft voice types can be difficult to understand by listeners due to lower vocal amplitude and pitch, particularly during teleconferences.
In general, the same reference numbers will be used throughout the drawing(s) and accompanying written description to refer to the same or like parts. The figures are not necessarily to scale.
Systems, methods, apparatus, and articles of manufacture described herein provide substantially real-time audio adjustment based on an identified voice type in captured audio. As used herein, the term “voice type” refers to a characterization of a person's speech based on voice characteristics that result from the vocal effort exerted in generating that speech. As used herein, the terms “voice type” and “vocal effort classification” are used interchangeably. As used herein, the term “vocal effort” is a quantity that corresponds to a perceived amount of vocal loudness and strain used by a speaker. Speakers generally use greater vocal effort when trying to speak in environments with a large amount of ambient noise, when speaking to someone far away, when speaking to a large group of people, when in a state of great emotional investment, and/or when attempting to get one or more listener(s) attention. Speakers generally use comparatively less vocal effort when speaking to someone close by, when trying to conceal their speech from potential eavesdroppers, when trying not to disturb surrounding persons, when in environments with low amounts of ambient noise, and/or when the speaker is calm.
Examples disclosed herein refer to five different voice types, namely, in order of most vocal effort to least vocal effort, (1) the yelled voice type, (2) the loud voice type, (3) the regular voice type, (4) the soft voice type, and (5) the whispered voice type. It should be appreciated that vocal efforts can be divided into different numbers of classifications (e.g., regular, above-regular, and below-regular, etc.) as needed for analysis. However, a vocal classification system may include any number or types of voice types.
The example regular voice type corresponds to speech delivered with regular vocal effort. The regular voice type is characterized by speech produced during normal conversations, typically in the absence of environmental or emotional stressing conditions on the speaker. The regular voice type has normal amplitude and pitch. The yelled voice type (e.g., the shouted voice type, the yelled vocal effort classification, etc.) corresponds to speech delivered with a yelled voice type.
The example yelled voice type is characterized by speech produced at high amplitude and pitch. The yelled voice type is typically used by people who perceive a high level of ambient noise (e.g., background noise, etc.) and/or people in high states of emotional investment (e.g., a speaker is experiencing great joy, a speaker is experiencing great anger, a speaker is in pain, a speaker is surprised, etc.).
The example loud voice type (e.g., the loud vocal effort classification, etc.) corresponds to a speech delivered with a loud voice type. The loud voice type is characterized by speech produced with substantially higher amplitude and slightly higher pitch than the regular voice type and can be associated with the speaker perceiving high amounts of background noise (e.g., the Lombard effect, etc.). The loud voice type can also be associated with a speaker being heavily invested in the conversation content (e.g., responding to a funny story, speaking from a position of authority, speaking during a heated argument, etc.). The loud voice type has comparatively lower amplitude and pitch than the yelled voice type.
The example soft voice type corresponds to speech delivered with a soft voice type. The soft voice type is characterized by speech produced with phonation (e.g., pitch, tone, harmonic variation, etc.), but with clear speaker-intended lower amplitude and lower pitch than speech in the regular voice type. The soft voice type is generally used by people to prevent others from eavesdropping, to not disturb nearby persons, and/or speech said to calm a listener.
The example whispered voice type corresponds to speech delivered with a whispered voice type. Unlike the soft voice type, the whispered voice type is characterized by speech produced without phonation and very low amplitude (e.g., minimum loudness, etc.) The use of the whispered voice type by a speaker implies a strong desire to not disturb other nearby people and/or a desire not to be overheard by potential eavesdroppers.
In recent years, video and audio telecommunications have become more common due to an increase in workers working from home and the decentralization of office structures. Participants in virtual meetings in spaces with other people (e.g., a home with multiple people working, an open plan office, a coffee shop, etc.) often prefer to speak with reduced loudness due to concerns for privacy and/or disturbing others in the shared space. Many of these participants prefer to speak with a soft voice type (e.g., with a vocal effort with reduced amplitude and pitch, etc.) instead of whispered voice type (e.g., a vocal effort with reduced amplitude and no phonation, etc.). While speakers using a soft voice type reduce the likelihood of other people in a shared space overhearing the speaker's conversation, other participants in telecommunications often have difficulty understanding the speaker because of the reduction in loudness, vocal quality, and intelligibility associated with the soft voice type. While non-soft voice identifying automatic gain control algorithms can increase the loudness of soft speech, such algorithms often take a comparatively large amount of time to adjust the audio and are only capable of adjusting the loudness (e.g., amplitude, etc.) of received audio. Additionally, because audio including soft voice type speech often includes more prominent noise than similar audio including normal vocal effort speech (e.g., because the speaker is in a shared space, etc.), increasing the gain also amplifies the noise in the received audio.
Examples disclosed herein modify received audio including soft voice type speech by identifying such speech including soft vocal effort and adjusting the audio based on the identification. In some examples disclosed herein, lightweight machine learning, neural net-based systems detect different voice types in real-time. In some examples disclosed herein, after identifying that received audio includes speech with a soft voice type, a predefined amount of gain is applied to the audio, which causes the remote listener to receive more intelligible audio substantially faster than non-soft voice identifying automatic gain control techniques. In some such examples disclosed herein, the predefined amount of gain is approximately 8 decibels. In some examples disclosed herein, after identifying that received audio includes speech with a soft voice type, a noise reduction algorithm is applied to the audio. In some examples disclosed herein, after identifying that received audio includes speech with a soft voice type, a soft voice type to normal voice type increase pitch transformation algorithm can be applied to the audio. Examples disclosed herein the quality of audio received by remote listeners over non-soft voice identifying automatic gain control techniques.
In the illustrated example of
The microphone 106 is a transducer that converts the sound emitted by a sound source (e.g., a speaker, etc.) into an audio signal. In the illustrated example, the microphone 106 is a component of the first user device 108. In other examples, the microphone 106 can be an independent device coupled to the first user device 108. In some examples, the microphone can include an audio-to-digital converter to digitize the input audio 104. The user devices 108, 118 are devices that can capture and/or output audio and/or video information. In some examples, the first user device 108 and/or the second user device 118 are associated with one or more speaker(s) (e.g., source(s) of the input audio 104, etc.) and one or more remote listener(s) (e.g., receiver(s) of the output audio 114, etc.). In some examples, the first user device 108 and/or the second user device 118 can be computer(s), a mobile device(s) (e.g., smartphone(s), tablet(s), etc.), navigation device(s) and/or wearable device(s) (e.g., smart watch(s), etc.). In the illustrated example of
The audio vocal effort classifier circuitry 112 analyzes the input audio 104 to determine the vocal effort associated with the input audio 104. For example, the audio vocal effort classifier circuitry 112 can be implemented by a classifier machine learning system. An example machine-learning system that can be used to implement the audio vocal effort classifier circuitry 112 is disclosed in U.S. patent application Ser. No. 18/176,252, which is hereby incorporated by reference in its entirety. In other examples, the audio vocal effort classifier circuitry 112 can be implemented by any other suitable vocal effort classifier system.
In the illustrated example of
The audio adjuster circuitry 102 processes the input audio 104 and the output of the audio vocal effort classifier circuitry 112. In some examples, the audio adjuster circuitry 102 can post-process the output of the audio vocal effort classifier circuitry 112 to determine a confidence value that the input audio 104 includes speech with a soft voice type. In some such examples, the audio vocal effort classifier circuitry 112 can determine if the input audio 104 includes speech with a soft voice type by comparing the confidence value to a threshold. In some examples, if the input audio 104 does not include speech with a soft voice type, the audio adjuster circuitry 102 can apply a non-soft voice identifying automatic gain control algorithm. In some examples, if the input audio 104 does include speech with a soft voice type, the audio adjuster circuitry 102 can apply a preset amount of gain to the input audio 104. Additionally or alternatively, if the input audio 104 does include speech with a soft voice type, the audio adjuster circuitry 102 can apply a noise reduction algorithm and/or a soft-voice type to a normal-voice type adjustment algorithm. In some examples, subject listener tests on the quality of different vocal efforts indicate that regular vocal effort speech has a higher perceived quality than soft vocal effort (e.g., regular vocal effort speech has a mean opinion score of 4.2/5 and soft vocal effort speech has a mean opinion score of 2.5/5, etc.). In some such examples, the audio adjuster circuitry 102 can significantly improve the quality of such soft vocal effort speech (e.g., gain normalized soft vocal effort speech has a mean opinion score of 3.8/5, gain normalized noise reduced vocal effort speech has a mean opinion score of 3.9/5, and pitch transformed, noise reduced speech has a mean opinion score of 4.0/5, etc.). An example implementation of the audio adjuster circuitry 102 is described below in conjunction with
In the illustrated example of
The classifier postprocessor circuitry 202 accesses the input audio 104 and corresponding audio classification data 119. For example, the classifier postprocessor circuitry 202 can continuously access the audio classification data 119 and/or the input audio 104 via the network 110. In other examples, the classifier postprocessor circuitry 202 can access the input audio 104 via a connection (e.g., a wired connection, a wireless connection, etc.) to the microphone 106 and/or the audio classification data 119 to the audio vocal effort classifier circuitry 112. In some examples, the classifier postprocessor circuitry 202 post-processes the audio classification data 119. For example, the classifier postprocessor circuitry 202 can filter, smooth, and/or weight the audio classification data 119 to generate a time-variate confidence value indicative of if the input audio 104 includes speech with a soft voice type. In some examples, the classifier postprocessor circuitry 202 can apply a moving average filter (e.g., a rolling average filter, a boxcar filter, a cumulative average filtered, a weighted moving average, etc.). Additionally or alternatively, the classifier postprocessor circuitry 202 can apply a smooth algorithm to the audio classification data 119. In other examples, the classifier postprocessor circuitry 202 can apply any other suitable post-processing to the audio classification data 119 (e.g., another filter accounting for previous inputs, a digital-to-analog converter, etc.). In some examples, the classifier postprocessor circuitry 202 is instantiated by programmable circuitry executing classifier postprocessor instructions and/or configured to perform operations such as those represented by the flowchart(s) of
In some examples, the audio adjuster circuitry 102 includes means for postprocessing audio classifier data. For example, the means for postprocessing audio classifier data may be implemented by classifier postprocessor circuitry 202. In some examples, the classifier postprocessor circuitry 202 may be instantiated by programmable circuitry such as the example programmable circuitry 712 of
The soft vocal effort identifier circuitry 204 determines if the current portion of the input audio 104 includes speech with a soft voice type. For example, the soft vocal effort identifier circuitry 204 can compare the output of the classifier postprocessor circuitry 202 to a threshold (e.g., the threshold 411 of
In some examples, the audio adjuster circuitry 102 includes means for identifying soft vocal effort. For example, the means for determining may be implemented by the soft vocal effort identifier circuitry 204. In some examples, the soft vocal effort identifier circuitry 204 may be instantiated by programmable circuitry such as the example programmable circuitry 712 of
The gain parameter adjuster circuitry 206 sets the soft voice type gain parameters. In some examples, the gain parameter adjuster circuitry 206 can set a preset gain based on the average difference in amplitude between the soft voice type and the normal voice type speech (e.g., the difference 304 of
In some examples, the audio adjuster circuitry 102 includes means for adjusting a gain value. For example, the means for adjusting a gain value may be implemented by the gain parameter adjuster circuitry 206. In some examples, the gain parameter adjuster circuitry 206 may be instantiated by programmable circuitry such as the example programmable circuitry 712 of
The gain control circuitry 208 applies one or more gains to the input audio 104. For example, the gain control circuitry 208 applies the soft voice type gain to the input audio 104. For example, the gain control circuitry 208 can increase the amplitude and/or loudness of the input audio 104 based on the voice type gain determined by the gain parameter adjuster circuitry 206. In some examples, the gain control circuitry 208 can apply automatic gain control (AGC) to the input audio 104. For example, the gain control circuitry 208 can apply a non-soft voice identifying AGC algorithm to the input audio 104. In some examples, the gain control circuitry 208 is instantiated by programmable circuitry executing gain control instructions and/or configured to perform operations such as those represented by the flowchart of
In some examples, the audio adjuster circuitry 102 includes means for controlling the gain applied to audio. For example, the means for controlling the gain applied to audio may be implemented by the gain control circuitry 208. In some examples, the gain control circuitry 208 may be instantiated by programmable circuitry such as the example programmable circuitry 712 of
The noise reducer circuitry 210 reduces the noise of the input audio 104. For example, because the soft vocal effort identifier circuitry 204 identified that the input audio includes speech with a soft voice type, the noise reducer circuitry 210 can apply a noise reduction algorithm to the input audio 104 to reduce the non-speech noise in the input audio 104. In some examples, the noise reducer circuitry 210 can remove frequency bands in the input audio 104 that are not associated with human speech. In some examples, the noise reducer circuitry 210 can use one or more filters to remove non-speech audio (e.g., a low pass filter, a band pass filter, a high pass filter, etc.). In some examples, the noise reducer circuitry 210 can use Wiener filtering. In some examples, the noise reducer circuitry 210 can use a machine-learning and/or artificial intelligence system to reduce the non-speech sounds in the input audio 104. In some examples, the noise reducer circuitry 210 is instantiated by programmable circuitry executing noise reduction instructions and/or configured to perform operations such as those represented by the flowchart of
In some examples, the audio adjuster circuitry 102 includes means for reducing noise. For example, the means for reducing noise may be implemented by the noise reducer circuitry 210. In some examples, the noise reducer circuitry 210 may be instantiated by programmable circuitry such as the example programmable circuitry 712 of
The audio transformer circuitry 212 adjusts the pitch of the input audio 104. For example, the audio transformer circuitry 212 can adjust the pitch of the input audio 104 from a range associated with soft vocal speech to a range associated with normal vocal effort speech. For example, speech with a soft voice type often has a lower pitch than normal vocal effort speech. In some such examples, the audio transformer circuitry 212 can increase the pitch of the input audio to a range associated with normal vocal effort speech. In some examples, the audio transformer circuitry 212 can determine the pitch adjustment based on vocal samples from a user of the first user device 108 (e.g., the current speaker, etc.). Additionally or alternatively, the audio transformer circuitry 212 can determine the pitch adjustment via a machine learning system. Additionally or alternatively, the audio transformer circuitry 212 can determine the pitch adjustment based on a preset pitch adjustment (e.g., based on empirical measurements of a plurality of speakers, etc.). In some examples, the audio transformer circuitry 212 adjusts the gain of the input audio 104. For example, the audio transformer circuitry 212 can increase the gain of the input audio by a present amount (e.g., the difference 304 of
In some examples, the audio adjuster circuitry includes means for transforming audio. For example, the means for transforming audio may be implemented by the audio transformer circuitry 212. In some examples, the audio transformer circuitry 212 may be instantiated by programmable circuitry such as the example programmable circuitry 712 of
The system interface circuitry 214 interfaces with system components of the user devices 108, 118. For example, the system interface circuitry 214 outputs the output audio 114. For example, the system interface circuitry 214 can output the output audio 114 generated by the gain control circuitry 208 during the execution of the block 508 and/or the execution of the block 512 via the speaker 116. In some examples, the system interface circuitry 214 is instantiated by programmable circuitry executing system interface instructions and/or configured to perform operations such as those represented by the flowchart of
In some examples, the audio adjuster circuitry 102 includes means for determining a condition of a device. For example, the means for determining may be implemented by the system interface circuitry 214. In some examples, t the system interface circuitry 214 may be instantiated by programmable circuitry such as the example programmable circuitry 712 of
While an example manner of implementing the audio adjuster circuitry of
In other examples, the audio adjuster circuitry 102 (e.g., the gain parameter adjuster circuitry 206, etc.) can determine the difference 304 on a per-user basis. In some such examples, the audio adjuster circuitry 102 can prompt a user of the first user device 108 to provide vocal samples with a normal voice type and a soft vocal effort (e.g., captured via the microphone 106, etc.). Additionally or alternatively, the audio adjuster circuitry 102 can determine the difference 304 in substantially real-time based on the output of the audio vocal effort classifier circuitry 112 (e.g., determining the average vocal difference between speech identified as a soft vocal effort by the audio vocal effort classifier circuitry 112 and speech identified as a normal vocal effort by the audio vocal effort classifier circuitry 112, etc.). In some examples, the audio adjuster circuitry 102 can determine the difference 304 based on the amplitudes of the soft voice samples and the normal voice samples associated with the user. In other examples, the audio adjuster circuitry 102 can determine the difference 304 in any other suitable manner.
In
In the illustrated example of
At the time 413, the soft vocal effort identifier circuitry 204 compares the value of the line 412 to the threshold 411 and based on this comparison, determines that the speaker is speaking with a soft voice type. In some such examples, the gain parameter adjuster circuitry 206 can generate a gain to compensate for the soft vocal effort of the speaker (e.g., 8 dB, the difference 304 of
In some examples, the threshold 411 and the postprocessing applied to the output of the audio vocal effort classifier circuitry 112 can be modified to adjust the length of the second duration 414. For example, the threshold 411 can be lowered (e.g., to 0.8, 0.5, etc.), and/or the filter can be weighted to increase the weight of more recent values to decrease the second duration 414. In some examples, the values of the threshold 411 and the parameters and/or type of filter used by the classifier postprocessor circuitry 202 of the audio adjuster circuitry 102 can be determined empirically to reduce the convergence time, while avoiding an undesirable amount of false identification of soft vocal efforts.
Flowchart(s) representative of example machine readable instructions, which may be executed by programmable circuitry to implement and/or instantiate the audio adjuster circuitry of
The program may be embodied in instructions (e.g., software and/or firmware) stored on one or more non-transitory computer readable and/or machine readable storage medium such as cache memory, a magnetic-storage device or disk (e.g., a floppy disk, a Hard Disk Drive (HDD), etc.), an optical-storage device or disk (e.g., a Blu-ray disk, a Compact Disk (CD), a Digital Versatile Disk (DVD), etc.), a Redundant Array of Independent Disks (RAID), a register, ROM, a solid-state drive (SSD), SSD memory, non-volatile memory (e.g., electrically erasable programmable read-only memory (EEPROM), flash memory, etc.), volatile memory (e.g., Random Access Memory (RAM) of any type, etc.), and/or any other storage device or storage disk. The instructions of the non-transitory computer readable and/or machine readable medium may program and/or be executed by programmable circuitry located in one or more hardware devices, but the entire program and/or parts thereof could alternatively be executed and/or instantiated by one or more hardware devices other than the programmable circuitry and/or embodied in dedicated hardware. The machine readable instructions may be distributed across multiple hardware devices and/or executed by two or more hardware devices (e.g., a server and a client hardware device). For example, the client hardware device may be implemented by an endpoint client hardware device (e.g., a hardware device associated with a human and/or machine user) or an intermediate client hardware device gateway (e.g., a radio access network (RAN)) that may facilitate communication between a server and an endpoint client hardware device. Similarly, the non-transitory computer readable storage medium may include one or more mediums. Further, although the example program is described with reference to the flowchart(s) illustrated in
The machine readable instructions described herein may be stored in one or more of a compressed format, an encrypted format, a fragmented format, a compiled format, an executable format, a packaged format, etc. Machine readable instructions as described herein may be stored as data (e.g., computer-readable data, machine-readable data, one or more bits (e.g., one or more computer-readable bits, one or more machine-readable bits, etc.), a bitstream (e.g., a computer-readable bitstream, a machine-readable bitstream, etc.), etc.) or a data structure (e.g., as portion(s) of instructions, code, representations of code, etc.) that may be utilized to create, manufacture, and/or produce machine executable instructions. For example, the machine readable instructions may be fragmented and stored on one or more storage devices, disks and/or computing devices (e.g., servers) located at the same or different locations of a network or collection of networks (e.g., in the cloud, in edge devices, etc.). The machine readable instructions may require one or more of installation, modification, adaptation, updating, combining, supplementing, configuring, decryption, decompression, unpacking, distribution, reassignment, compilation, etc., in order to make them directly readable, interpretable, and/or executable by a computing device and/or other machine. For example, the machine readable instructions may be stored in multiple parts, which are individually compressed, encrypted, and/or stored on separate computing devices, wherein the parts when decrypted, decompressed, and/or combined form a set of computer-executable and/or machine executable instructions that implement one or more functions and/or operations that may together form a program such as that described herein.
In another example, the machine readable instructions may be stored in a state in which they may be read by programmable circuitry, but require addition of a library (e.g., a dynamic link library (DLL)), a software development kit (SDK), an application programming interface (API), etc., in order to execute the machine-readable instructions on a particular computing device or other device. In another example, the machine readable instructions may need to be configured (e.g., settings stored, data input, network addresses recorded, etc.) before the machine readable instructions and/or the corresponding program(s) can be executed in whole or in part. Thus, machine readable, computer readable and/or machine readable media, as used herein, may include instructions and/or program(s) regardless of the particular format or state of the machine readable instructions and/or program(s).
The machine readable instructions described herein can be represented by any past, present, or future instruction language, scripting language, programming language, etc. For example, the machine readable instructions may be represented using any of the following languages: C, C++, Java, C #, Perl, Python, JavaScript, HyperText Markup Language (HTML), Structured Query Language (SQL), Swift, etc.
As mentioned above, the example operations of
“Including” and “comprising” (and all forms and tenses thereof) are used herein to be open ended terms. Thus, whenever a claim employs any form of “include” or “comprise” (e.g., comprises, includes, comprising, including, having, etc.) as a preamble or within a claim recitation of any kind, it is to be understood that additional elements, terms, etc., may be present without falling outside the scope of the corresponding claim or recitation. As used herein, when the phrase “at least” is used as the transition term in, for example, a preamble of a claim, it is open-ended in the same manner as the term “comprising” and “including” are open ended. The term “and/or” when used, for example, in a form such as A, B, and/or C refers to any combination or subset of A, B, C such as (1) A alone, (2) B alone, (3) C alone, (4) A with B, (5) A with C, (6) B with C, or (7) A with B and with C. As used herein in the context of describing structures, components, items, objects and/or things, the phrase “at least one of A and B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, or (3) at least one A and at least one B. Similarly, as used herein in the context of describing structures, components, items, objects and/or things, the phrase “at least one of A or B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, or (3) at least one A and at least one B. As used herein in the context of describing the performance or execution of processes, instructions, actions, activities, etc., the phrase “at least one of A and B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, or (3) at least one A and at least one B. Similarly, as used herein in the context of describing the performance or execution of processes, instructions, actions, activities, etc., the phrase “at least one of A or B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, or (3) at least one A and at least one B.
As used herein, singular references (e.g., “a”, “an”, “first”, “second”, etc.) do not exclude a plurality. The term “a” or “an” object, as used herein, refers to one or more of that object. The terms “a” (or “an”), “one or more”, and “at least one” are used interchangeably herein. Furthermore, although individually listed, a plurality of means, elements, or actions may be implemented by, e.g., the same entity or object. Additionally, although individual features may be included in different examples or claims, these may possibly be combined, and the inclusion in different examples or claims does not imply that a combination of features is not feasible and/or advantageous.
The example machine-readable instructions and/or the example operations 500 of
At block 506, the soft vocal effort identifier circuitry 204 determines if the current portion of the input audio 104 includes speech with a soft voice type. For example, the soft vocal effort identifier circuitry 204 can compare the output of the classifier postprocessor circuitry 202 to a threshold (e.g., the threshold 411 of
At block 510, the gain parameter adjuster circuitry 206 sets the soft voice type gain parameters. In some examples, the gain parameter adjuster circuitry 206 can set a preset gain based on the average difference in amplitude between soft voice type and the normal voice type speech (e.g., the difference 304 of
At block 514, the system interface circuitry 214 outputs the output audio 114. For example, the system interface circuitry 214 can output the output audio 114 generated by the gain control circuitry 208 during the execution of the block 508 and/or the execution of the block 512 via the speaker 116. At block 516, the system interface circuitry 214 and/or the classifier postprocessor circuitry 202 can determine if there is additional information to be processed. For example, the system interface circuitry 214 and/or the classifier postprocessor circuitry 202 can determine if the input audio 104 and/or the audio classification data 119 is still being accessed. In other examples, the system interface circuitry 214 and/or the classifier postprocessor circuitry 202 can be determined based on a power status of the first user device 108, the second user device 118, and/or a conferencing service executing on one or both of the user devices 108, 118. If the system interface circuitry 214 and/or the classifier postprocessor circuitry 202 determines there is additional audio to be processed, the operations 500 return to block 504. If the system interface circuitry 214 and/or the classifier postprocessor circuitry 202 determines there is no additional audio to be processed, the operations 500 end.
The example machine-readable instructions and/or the example operations 500 of
At block 606, the soft vocal effort identifier circuitry 204 determines if the current portion of the input audio 104 includes speech with a soft voice type. For example, the soft vocal effort identifier circuitry 204 can compare the output of the classifier postprocessor circuitry 202 to a threshold (e.g., the threshold 411 of
At block 610, the noise reducer circuitry 210 reduces the noise of the input audio 104. For example, because the soft vocal effort identifier circuitry 204 identified that the input audio includes speech with a soft voice type, the noise reducer circuitry 210 can apply a noise reduction algorithm to the input audio 104 to reduce the non-speech noise in the input audio 104. In some examples, the noise reducer circuitry 210 can remove frequency bands in the input audio 104 that are not associated with human speech. In some examples, the noise reducer circuitry 210 can use one or more filters to remove non-speech audio (e.g., a low pass filter, a band pass filter, a high pass filter, etc.). In some examples, the noise reducer circuitry 210 can use Wiener filtering. In some examples, the noise reducer circuitry 210 can use a machine-learning and/or artificial intelligence system to reduce the non-speech sounds in the input audio 104.
At block 612, the audio transformer circuitry 212 adjusts the pitch of the input audio 104. For example, the audio transformer circuitry 212 can adjust the pitch of the input audio 104 from a range associated with soft vocal speech to a range associated with normal vocal effort speech. For example, speech with a soft voice type often has a lower pitch than normal vocal effort speech. In some such examples, the audio transformer circuitry 212 can increase the pitch of the input audio to a range associated with normal vocal effort speech. In some examples, the audio transformer circuitry 212 can determine the pitch adjustment based on vocal samples from a user of the first user device 108 (e.g., the current speaker, etc.). Additionally or alternatively, the audio transformer circuitry 212 can determine the pitch adjustment via a machine learning system. Additionally or alternatively, the audio transformer circuitry 212 can determine the pitch adjustment based on a preset pitch adjustment (e.g., based on empirical measurements of a plurality of speakers, etc.).
At block 614, the audio transformer circuitry 212 adjusts the gain of the input audio 104. For example, the audio transformer circuitry 212 can increase the gain of the input audio by a present amount (e.g., the difference 304 of
At block 616, the system interface circuitry 214 outputs the output audio 114. For example, the system interface circuitry 214 can output the output audio 114 generated during the execution of the block 608 and/or the executions of the blocks 610, 612, 614 via the speaker 116. At block 618, the system interface circuitry 214 and/or the classifier postprocessor circuitry 202 can determine if there is additional information to be processed. For example, the system interface circuitry 214 and/or the classifier postprocessor circuitry 202 can determine if the input audio 104 and/or the audio classification data 119 is still being accessed. In other examples, the system interface circuitry 214 and/or the classifier postprocessor circuitry 202 can be determined based on a power status of the first user device 108, the second user device 118, and/or a conferencing service executing on one or both of the user devices 108, 118. If the system interface circuitry 214 and/or the classifier postprocessor circuitry 202 determines there is additional audio to be processed, the operations 600 return to block 604. If the system interface circuitry 214 and/or the classifier postprocessor circuitry 202 determines there is no additional audio to be processed, the operations 600 end.
The programmable circuitry platform 700 of the illustrated example includes programmable circuitry 712. The programmable circuitry 712 of the illustrated example is hardware. For example, the programmable circuitry 712 can be implemented by one or more integrated circuits, logic circuits, FPGAs, microprocessors, CPUs, GPUs, DSPs, and/or microcontrollers from any desired family or manufacturer. The programmable circuitry 712 may be implemented by one or more semiconductor based (e.g., silicon based) devices. In this example, the programmable circuitry 712 implements the classifier postprocessor circuitry 202, the soft vocal effort identifier circuitry 204, the gain parameter adjuster circuitry 206, the gain control circuitry 208, the noise reducer circuitry 210, the audio transformer circuitry 212, and the system interface circuitry 214.
The programmable circuitry 712 of the illustrated example includes a local memory 713 (e.g., a cache, registers, etc.). The programmable circuitry 712 of the illustrated example is in communication with main memory 714, 716, which includes a volatile memory 714 and a non-volatile memory 716, by a bus 718. The volatile memory 714 may be implemented by Synchronous Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS® Dynamic Random Access Memory (RDRAM®), and/or any other type of RAM device. The non-volatile memory 716 may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory 714, 716 of the illustrated example is controlled by a memory controller 717. In some examples, the memory controller 717 may be implemented by one or more integrated circuits, logic circuits, microcontrollers from any desired family or manufacturer, or any other type of circuitry to manage the flow of data going to and from the main memory 714, 716.
The programmable circuitry platform 700 of the illustrated example also includes interface circuitry 720. The interface circuitry 720 may be implemented by hardware in accordance with any type of interface standard, such as an Ethernet interface, a universal serial bus (USB) interface, a Bluetooth® interface, a near field communication (NFC) interface, a Peripheral Component Interconnect (PCI) interface, and/or a Peripheral Component Interconnect Express (PCIe) interface.
In the illustrated example, one or more input devices 722 are connected to the interface circuitry 720. The input device(s) 722 permit(s) a user (e.g., a human user, a machine user, etc.) to enter data and/or commands into the programmable circuitry 712. The input device(s) 722 can be implemented by, for example, an audio sensor, a microphone, a camera (still or video), a keyboard, a button, a mouse, a touchscreen, a trackpad, a trackball, an isopoint device, and/or a voice recognition system.
One or more output devices 724 are also connected to the interface circuitry 720 of the illustrated example. The output device(s) 724 can be implemented, for example, by display devices (e.g., a light emitting diode (LED), an organic light emitting diode (OLED), a liquid crystal display (LCD), a cathode ray tube (CRT) display, an in-place switching (IPS) display, a touchscreen, etc.), a tactile output device, a printer, and/or speaker. The interface circuitry 720 of the illustrated example, thus, typically includes a graphics driver card, a graphics driver chip, and/or graphics processor circuitry such as a GPU.
The interface circuitry 720 of the illustrated example also includes a communication device such as a transmitter, a receiver, a transceiver, a modem, a residential gateway, a wireless access point, and/or a network interface to facilitate exchange of data with external machines (e.g., computing devices of any kind) by a network 726. The communication can be by, for example, an Ethernet connection, a digital subscriber line (DSL) connection, a telephone line connection, a coaxial cable system, a satellite system, a beyond-line-of-sight wireless system, a line-of-sight wireless system, a cellular telephone system, an optical connection, etc.
The programmable circuitry platform 700 of the illustrated example also includes one or more mass storage discs or devices 728 to store firmware, software, and/or data. Examples of such mass storage discs or devices 728 include magnetic storage devices (e.g., floppy disk, drives, HDDs, etc.), optical storage devices (e.g., Blu-ray disks, CDs, DVDs, etc.), RAID systems, and/or solid-state storage discs or devices such as flash memory devices and/or SSDs.
The machine readable instructions 732, which may be implemented by the machine readable instructions of
The cores 802 may communicate by a first example bus 804. In some examples, the first bus 804 may be implemented by a communication bus to effectuate communication associated with one(s) of the cores 802. For example, the first bus 804 may be implemented by at least one of an Inter-Integrated Circuit (I2C) bus, a Serial Peripheral Interface (SPI) bus, a PCI bus, or a PCIe bus. Additionally or alternatively, the first bus 804 may be implemented by any other type of computing or electrical bus. The cores 802 may obtain data, instructions, and/or signals from one or more external devices by example interface circuitry 806. The cores 802 may output data, instructions, and/or signals to the one or more external devices by the interface circuitry 806. Although the cores 802 of this example include example local memory 820 (e.g., Level 1 (L1) cache that may be split into an L1 data cache and an L1 instruction cache), the microprocessor 800 also includes example shared memory 810 that may be shared by the cores (e.g., Level 2 (L2 cache)) for high-speed access to data and/or instructions. Data and/or instructions may be transferred (e.g., shared) by writing to and/or reading from the shared memory 810. The local memory 820 of each of the cores 802 and the shared memory 810 may be part of a hierarchy of storage devices including multiple levels of cache memory and the main memory (e.g., the main memory 714, 716 of
Each core 802 may be referred to as a CPU, DSP, GPU, etc., or any other type of hardware circuitry. Each core 802 includes control unit circuitry 814, arithmetic and logic (AL) circuitry 816 (sometimes referred to as an ALU), a plurality of registers 818, the local memory 820, and a second example bus 822. Other structures may be present. For example, each core 802 may include vector unit circuitry, single instruction multiple data (SIMD) unit circuitry, load/store unit (LSU) circuitry, branch/jump unit circuitry, floating-point unit (FPU) circuitry, etc. The control unit circuitry 814 includes semiconductor-based circuits structured to control (e.g., coordinate) data movement within the corresponding core 802. The AL circuitry 816 includes semiconductor-based circuits structured to perform one or more mathematic and/or logic operations on the data within the corresponding core 802. The AL circuitry 816 of some examples performs integer based operations. In other examples, the AL circuitry 816 also performs floating-point operations. In yet other examples, the AL circuitry 816 may include first AL circuitry that performs integer-based operations and second AL circuitry that performs floating-point operations. In some examples, the AL circuitry 816 may be referred to as an Arithmetic Logic Unit (ALU).
The registers 818 are semiconductor-based structures to store data and/or instructions such as results of one or more of the operations performed by the AL circuitry 816 of the corresponding core 802. For example, the registers 818 may include vector register(s), SIMD register(s), general-purpose register(s), flag register(s), segment register(s), machine-specific register(s), instruction pointer register(s), control register(s), debug register(s), memory management register(s), machine check register(s), etc. The registers 818 may be arranged in a bank as shown in
Each core 802 and/or, more generally, the microprocessor 800 may include additional and/or alternate structures to those shown and described above. For example, one or more clock circuits, one or more power supplies, one or more power gates, one or more cache home agents (CHAs), one or more converged/common mesh stops (CMSs), one or more shifters (e.g., barrel shifter(s)) and/or other circuitry may be present. The microprocessor 800 is a semiconductor device fabricated to include many transistors interconnected to implement the structures described above in one or more integrated circuits (ICs) contained in one or more packages.
The microprocessor 800 may include and/or cooperate with one or more accelerators (e.g., acceleration circuitry, hardware accelerators, etc.). In some examples, accelerators are implemented by logic circuitry to perform certain tasks more quickly and/or efficiently than can be done by a general-purpose processor. Examples of accelerators include ASICs and FPGAs such as those discussed herein. A GPU, DSP and/or other programmable device can also be an accelerator. Accelerators may be on-board the microprocessor 800, in the same chip package as the microprocessor 800 and/or in one or more separate packages from the microprocessor 800.
More specifically, in contrast to the microprocessor 800 of
In the example of
In some examples, the binary file is compiled, generated, transformed, and/or otherwise output from a uniform software platform utilized to program FPGAs. For example, the uniform software platform may translate first instructions (e.g., code or a program) that correspond to one or more operations/functions in a high-level language (e.g., C, C++, Python, etc.) into second instructions that correspond to the one or more operations/functions in an HDL. In some such examples, the binary file is compiled, generated, and/or otherwise output from the uniform software platform based on the second instructions. In some examples, the FPGA circuitry 900 of
The FPGA circuitry 900 of
The FPGA circuitry 900 also includes an array of example logic gate circuitry 908, a plurality of example configurable interconnections 910, and example storage circuitry 912. The logic gate circuitry 908 and the configurable interconnections 910 are configurable to instantiate one or more operations/functions that may correspond to at least some of the machine readable instructions of
The configurable interconnections 910 of the illustrated example are conductive pathways, traces, vias, or the like that may include electrically controllable switches (e.g., transistors) whose state can be changed by programming (e.g., using an HDL instruction language) to activate or deactivate one or more connections between one or more of the logic gate circuitry 908 to program desired logic circuits.
The storage circuitry 912 of the illustrated example is structured to store result(s) of the one or more of the operations performed by corresponding logic gates. The storage circuitry 912 may be implemented by registers or the like. In the illustrated example, the storage circuitry 912 is distributed amongst the logic gate circuitry 908 to facilitate access and increase execution speed.
The example FPGA circuitry 900 of
Although
It should be understood that some or all of the circuitry of
In some examples, some or all of the circuitry of
In some examples, the programmable circuitry 712 of
A block diagram illustrating an example software distribution platform 1005 to distribute software such as the example machine readable instructions 732 of
As used in this patent, stating that any part (e.g., a layer, film, area, region, or plate) is in any way on (e.g., positioned on, located on, disposed on, or formed on, etc.) another part, indicates that the referenced part is either in contact with the other part, or that the referenced part is above the other part with one or more intermediate part(s) located therebetween.
As used herein, connection references (e.g., attached, coupled, connected, and joined) may include intermediate members between the elements referenced by the connection reference and/or relative movement between those elements unless otherwise indicated. As such, connection references do not necessarily infer that two elements are directly connected and/or in fixed relation to each other. As used herein, stating that any part is in “contact” with another part is defined to mean that there is no intermediate part between the two parts.
Unless specifically stated otherwise, descriptors such as “first,” “second,” “third,” etc., are used herein without imputing or otherwise indicating any meaning of priority, physical order, arrangement in a list, and/or ordering in any way, but are merely used as labels and/or arbitrary names to distinguish elements for ease of understanding the disclosed examples. In some examples, the descriptor “first” may be used to refer to an element in the detailed description, while the same element may be referred to in a claim with a different descriptor such as “second” or “third.” In such instances, it should be understood that such descriptors are used merely for identifying those elements distinctly within the context of the discussion (e.g., within a claim) in which the elements might, for example, otherwise share a same name.
As used herein, “approximately” and “about” modify their subjects/values to recognize the potential presence of variations that occur in real world applications. For example, “approximately” and “about” may modify dimensions that may not be exact due to manufacturing tolerances and/or other real world imperfections as will be understood by persons of ordinary skill in the art. For example, “approximately” and “about” may indicate such dimensions may be within a tolerance range of +/−10% unless otherwise specified in the below description.
As used herein “substantially real time” refers to occurrence in a near instantaneous manner recognizing there may be real world delays for computing time, transmission, etc. Thus, unless otherwise specified, “substantially real time” refers to real time+1 second.
As used herein, the phrase “in communication,” including variations thereof, encompasses direct communication and/or indirect communication through one or more intermediary components, and does not require direct physical (e.g., wired) communication and/or constant communication, but rather additionally includes selective communication at periodic intervals, scheduled intervals, aperiodic intervals, and/or one-time events.
As used herein, “programmable circuitry” is defined to include (i) one or more special purpose electrical circuits (e.g., an application specific circuit (ASIC)) structured to perform specific operation(s) and including one or more semiconductor-based logic devices (e.g., electrical hardware implemented by one or more transistors), and/or (ii) one or more general purpose semiconductor-based electrical circuits programmable with instructions to perform specific functions(s) and/or operation(s) and including one or more semiconductor-based logic devices (e.g., electrical hardware implemented by one or more transistors). Examples of programmable circuitry include programmable microprocessors such as Central Processor Units (CPUs) that may execute first instructions to perform one or more operations and/or functions, Field Programmable Gate Arrays (FPGAs) that may be programmed with second instructions to cause configuration and/or structuring of the FPGAs to instantiate one or more operations and/or functions corresponding to the first instructions, Graphics Processor Units (GPUs) that may execute first instructions to perform one or more operations and/or functions, Digital Signal Processors (DSPs) that may execute first instructions to perform one or more operations and/or functions, XPUs, Network Processing Units (NPUs) one or more microcontrollers that may execute first instructions to perform one or more operations and/or functions and/or integrated circuits such as Application Specific Integrated Circuits (ASICs). For example, an XPU may be implemented by a heterogeneous computing system including multiple types of programmable circuitry (e.g., one or more FPGAs, one or more CPUs, one or more GPUs, one or more NPUs, one or more DSPs, etc., and/or any combination(s) thereof), and orchestration technology (e.g., application programming interface(s) (API(s)) that may assign computing task(s) to whichever one(s) of the multiple types of programmable circuitry is/are suited and available to perform the computing task(s).
As used herein integrated circuit/circuitry is defined as one or more semiconductor packages containing one or more circuit elements such as transistors, capacitors, inductors, resistors, current paths, diodes, etc. For example an integrated circuit may be implemented as one or more of an ASIC, an FPGA, a chip, a microchip, programmable circuitry, a semiconductor substrate coupling multiple circuit elements, a system on chip (SoC), etc.
Methods and apparatus to audio adjustment based on vocal effort are disclosed herein. Further examples and combinations thereof include the following:
Example 1 includes an apparatus comprising interface circuitry, machine readable instructions, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to identify a speech with a soft voice type in audio from a first user device, the speech with a soft voice type including phonation, modify the audio to generate modified audio based on the identification of the speech with a soft voice type, and output the modified audio from a second user device.
Example 2 includes the apparatus of example 1, wherein the programmable circuitry is to at least one of instantiate or execute the machine readable instructions to identify the speech with a soft voice type of the audio of the audio by accessing an output of an audio vocal effort classifier based on an input of the audio, generating a postprocessed vocal classification output by apply a moving average filter to the output of the vocal classification model, and comparing the postprocessed vocal classification output to a threshold.
Example 3 includes the apparatus of example 1, wherein the output of the audio vocal effort classifier includes a binary output corresponding to a presence of the speech with a soft voice type.
Example 4 includes the apparatus of example 1, wherein the programmable circuitry is to at least one of instantiate or execute the machine readable instructions to modify the audio by applying a preset gain to the audio.
Example 5 includes the apparatus of example 4, wherein the preset gain is approximately 8 decibels.
Example 6 includes the apparatus of example 1, wherein the programmable circuitry is to at least one of instantiate or execute the machine readable instructions to modify the audio by reducing non-speech noise in the audio.
Example 7 includes the apparatus of example 1, wherein the programmable circuitry is to at least one of instantiate or execute the machine readable instructions to modify the audio by increasing a pitch of the audio.
Example 8 includes a non-transitory machine readable storage medium comprising instructions to cause programmable circuitry to at least identify a speech with a soft voice type in audio from a first user device, the speech with a soft voice type including phonation, modify the audio to generate modified audio based on the identification of the speech with a soft voice type, and output the modified audio from a second user device.
Example 9 includes the non-transitory machine readable storage medium of example 8, wherein the instructions are to cause the programmable circuitry to access an output of an audio vocal effort classifier based on an input of the audio, generate a postprocessed vocal classification output by apply a moving average filter to the output of the vocal classification model, and compare the postprocessed vocal classification output to a threshold.
Example 10 includes the non-transitory machine readable storage medium of example 8, the output of the audio vocal effort classifier model includes a binary output corresponding to a presence of the speech with a soft voice type.
Example 11 includes the non-transitory machine readable storage medium of example 8, wherein the instructions are to cause the programmable circuitry to modify the audio by applying a preset gain to the audio.
Example 12 includes the non-transitory machine readable storage medium of example 12, wherein the preset gain is approximately 8 decibels.
Example 13 includes the non-transitory machine readable storage medium of example 8, wherein the instructions are to cause the programmable circuitry to modify the audio by reducing non-speech noise in the audio.
Example 14 includes the non-transitory machine readable storage medium of example 8, wherein the instructions are to cause the programmable circuitry to modify the audio by increasing a pitch of the audio.
Example 15 includes a method comprising identifying a speech with a soft voice type in audio from a first user device, the speech with a soft voice type including phonation, modifying the audio to generate modified audio based on the identification of the speech with a soft voice type, and outputting the modified audio from a second user device.
Example 16 includes the method of example 15, wherein the identifying the speech with a soft voice type of the audio of the audio includes accessing an output of a vocal classification model based on an input of the audio, generating a postprocessed output by apply a moving average filter to the output of the vocal classification model, and comparing the postprocessed output to a threshold.
Example 17 includes the method of example 15, wherein the output of a vocal classification model includes a binary output corresponding to a presence of the speech with a soft voice type.
Example 18 includes the method of example 15, wherein the modifying the audio includes applying a preset gain to the audio.
Example 19 includes the method of example 15, wherein the modifying the audio includes reducing non-speech noise in the audio.
Example 20 includes the method of example 15, wherein the modifying the audio includes increasing a pitch of the audio.
The following claims are hereby incorporated into this Detailed Description by this reference. Although certain example systems, apparatus, articles of manufacture, and methods have been disclosed herein, the scope of coverage of this patent is not limited thereto. On the contrary, this patent covers all systems, apparatus, articles of manufacture, and methods fairly falling within the scope of the claims of this patent.
