Patent Application
20220180892
The present document relates to techniques for identifying multimedia content and associated information on a television device or a video server delivering multimedia content, and enabling embedded software applications to utilize the multimedia content to provide content and services synchronous with that multimedia content. Various embodiments relate to methods and systems for providing automated audio analysis to identify and extract information from television programming content depicting sporting events, so as to create metadata associated with video highlights for in-game and post-game viewing.
Enhanced television applications such as interactive advertising and enhanced program guides with pre-game, in-game and post-game interactive applications have long been envisioned. Existing cable systems that were originally engineered for broadcast television are being called on to support a host of new applications and services including interactive television services and enhanced (interactive) programming guides.
Some frameworks for enabling enhanced television applications have been standardized. Examples include the OpenCable™ Enhanced TV Application Messaging Specification, as well as the Tru2way specification, which refer to interactive digital cable services delivered over a cable video network and which include features such as interactive program guides, interactive ads, games, and the like. Additionally, cable operator “OCAP” programs provide interactive services such as e-commerce shopping, online banking, electronic program guides, and digital video recording. These efforts have enabled the first generation of video-synchronous applications, synchronized with video content delivered by the programmer/broadcaster, and providing added data and interactivity to television programming.
Recent developments in video/audio content analysis technologies and capable mobile devices have opened up an array of new possibilities in developing sophisticated applications that operate synchronously with live TV programming events. These new technologies and advances in audio signal processing and computer vision, as well as improved computing power of modern processors, allow for real-time generation of sophisticated programming content highlights accompanied by metadata that are currently lacking in the television and other media environments.
A system and method are presented to enable automatic real-time processing of audio signals extracted from sporting event television programming content, for detecting, selecting, and tracking short bursts of high-energy audio events, such as tennis ball hits in a tennis match.
In at least one embodiment, initial audio signal analysis is performed in the time domain, so as to detect short bursts of high-energy audio and generate an indicator of potential occurrence of audio events of interest.
In at least one embodiment, detected time-domain audio events are further processed and revised by invoking consideration of spectral characteristics of the audio signal in the neighborhood of detected time-domain audio events. A spectrogram is constructed for the analyzed audio signal, and pronounced spectral magnitude peaks are extracted by maximum magnitude suppression in a sliding 2-D diamond-shaped time-frequency area filter. In addition, a spectrogram time-spread range is constructed around audio event points previously obtained by the time-domain analysis, and a qualifier for each audio event point is established by counting spectral magnitude peaks in this time-spread range. The time-spread range can be established in any of a multitude of ways; for example, the spectral neighborhood of the time-domain detected audio events can be analyzed immediately before the audio event occurred, or immediately after the audio event occurred, or in a time and frequency range around the detected audio event. In one embodiment, as an exemplary case, only audio events obtained by time-domain analysis with associated qualifier value below a threshold are accepted as viable audio events.
Any of a number of spectral neighborhood analysis methods can be applied, including, but not limited to, spectral analysis performed by counting pronounced spectral peaks in various time-spread ranges in the neighborhoods of detected time-domain audio events, as described in the previous paragraph.
In at least one embodiment, a schedule of minimal time distance between adjacent audio event points is considered. Undesirable redundant audio events that are in close proximity to each other are removed, and a final audio event timeline for the game is formed.
In at least one embodiment, once the audio event information has been extracted, it is automatically appended to sporting event metadata associated with the sporting event video highlights, and can be subsequently used in connection with automatic generation of highlights.
In at least one embodiment, a method may be used to identify a boundary of a highlight of audiovisual content depicting an event. The method may include, at a data store, storing audio data depicting at least part of the event. The method may further include, at a processor, automatically analyzing the audio data to detect an audio event indicative of an occurrence to be included in the highlight, and designating a time index, within the audiovisual content, before or after the audio event as the boundary, the boundary comprising one of a beginning of the highlight and an end of the highlight.
The audiovisual content may include a television broadcast.
The audiovisual content may include an audiovisual stream. The method may further include, prior to storing audio data depicting at least part of the event, extracting the audio data from the audiovisual stream.
The audiovisual content may include stored audiovisual content. The method may further include, prior to storing audio data depicting at least part of the event, extracting the audio data from the stored audiovisual content.
In at least one embodiment, the event may be a sporting event. The highlight may depict a portion of the sporting event deemed to be of particular interest to at least one user. The occurrence may be any occurrence associated with a sporting event, such as for example a tennis serve.
The method may further include, at an output device, playing at least one of the audiovisual content and the highlight during detection of the audio event.
The method may further include, prior to detecting the audio event, pre-processing the audio data by resampling the audio data to a desired sampling rate.
The method may further include, prior to detecting the audio event, pre-processing the audio data by filtering the audio data to perform at least one of reducing noise, and selecting a spectral band of interest.
Automatically analyzing the audio data to detect the audio events may include processing the audio data, in a time domain, to generate initial row indicators of occurrences of distinct energy burst events.
Processing the audio data may include selecting an analysis time window size, selecting an analysis window overlap region size, sliding an analysis time window along the audio data, computing a normalized magnitude for window samples at each position of the analysis time window, calculating an average sample magnitude at each position of the analysis time window, generating a log magnitude indicator at each position of the analysis time window, and using the normalized magnitude, average sample magnitude, and log magnitude indicator to populate a row time-domain event vector with a computed indicator and associated position values.
The method may further include processing the audio data to generate a spectrogram for the audio data, and analyzing the audio data and the spectrogram in a joint time-frequency domain to generate qualifying indicators of occurrences of the audio events, comprising distinct energy burst events detected in the time domain.
Analyzing the audio data and the spectrogram in the joint time-frequency domain may include constructing a 2-D diamond-shaped spectrogram area filter to facilitate detection and selection of pronounced time-frequency magnitude peaks, sliding the area filter along time and frequency spectrogram axes, checking a central peak magnitude against all remaining peak magnitudes at each time-frequency position of the area filter, retaining only central peak magnitudes that are greater than all other peak magnitudes at each time-frequency position of the area filter, and populating a spectral event vector with all retained central peak magnitudes.
The method may further include, in the time domain and in a frequency domain, performing joint analysis of audio events detected in the time domain.
The method may further include determining a spectrogram time-spread range around each of the audio events, and using the time-spread ranges for event qualifier computation.
Using the time-spread ranges for event qualifier computation may include counting spectral event vector elements positioned in the spectrogram time-spread range around the audio events detected in the time domain, recording the spectral event vector elements as qualifiers for each of the audio events, counting a number of spectrogram magnitude peaks within a time spread range to obtain a count, and generating a revised event vector containing only time-domain event points at which the count is below a threshold.
Using the time-spread ranges for event qualifier computation may further include comparing the qualifier associated with each of the audio events detected in the time domain, against a threshold, suppressing all time-domain detected events with a qualifier above the threshold, and generating a qualifier revised event vector.
The method may further include processing the qualifier revised event vector according to a schedule of minimal time distances between adjacent events, and suppressing undesirable, redundant audio events to obtain a final desired event timeline for the event.
The method may further include automatically appending at least one of the audio events, the time index, and an indicator of the occurrence to metadata associated with the highlight.
In at least one embodiment, the occurrence may be associated with a short audio burst.
In at least one embodiment, the event may be a sporting event. For example, the event may be a tennis game, and the occurrence may be a tennis serve.
Further details and variations are described herein.
The accompanying drawings, together with the description, illustrate several embodiments. One skilled in the art will recognize that the particular embodiments illustrated in the drawings are merely exemplary, and are not intended to limit scope.
The following definitions are presented for explanatory purposes only, and are not intended to limit scope.
According to various embodiments, methods and systems are provided for automatically creating time-based metadata associated with highlights of television programming of a sporting event or the like, wherein such video highlights and associated metadata are generated synchronously with the television broadcast of a sporting event or the like, or while the sporting event video content is being streamed via a video server from a storage device after the television broadcast of a sporting event.
In at least one embodiment, an automated video highlights and associated metadata generation application may receive a live broadcast audiovisual stream, or a digital audiovisual stream received via a computer server. The application may then process an extracted audio signal, for example using digital signal processing techniques, to detect short bursts of high energy audio events, such as tennis ball hits in a tennis match or the like.
Interactive television applications may enable timely, relevant presentation of highlighted television programming content to users watching television programming either on a primary television display, or on a secondary display such as tablet, laptop or a smartphone. In at least one embodiment, a set of video clips representing television broadcast content highlights may be generated and/or stored in real-time, along with a database containing time-based metadata describing, in more detail, the occurrences presented by the highlight video clips.
In various embodiments, the metadata accompanying the video clips can be any information such as textual information, images, and/or any type of audiovisual data. Metadata may be associated with in-game and/or post-game video content highlights, and may present occurrences detected by real-time processing of audio signals extracted from sporting event television programming. Event information may be detected by analyzing an audio signal to identify key occurrences in the game, such as important plays. Audio events indicating such key occurrences may include, for example, tennis ball hits in tennis matches, or a cheering crowd noise following an audio event, audio announcements, music, and/or the like. In various embodiments, the system and method described herein enable automatic metadata generation and video highlight processing, wherein boundaries of audio events (for example, the beginning or end of an audio event) can be detected and determined by analyzing a digital audio stream.
In at least one embodiment, a system receives a broadcast audiovisual stream, or other audiovisual content obtained via a computer server, extracts an audio portion of the audiovisual stream or content, and processes the extracted audio signal using digital signal processing techniques, so as to detect distinct high-energy audio bursts, such as for example those associated with tennis ball hits in tennis games. Such processing can include, for example, any or all of the following steps:
In at least one embodiment, an initial audio signal analysis is performed in the time domain, so as to detect short bursts of high-energy audio and generate of audio events representing potential exciting occurrences. An analyzing time window of a selected size may be used to compute an indicator of the average level of audio energy at overlapping window positions. Subsequently, a row event vector may be populated with indicator/position pairs.
In at least one embodiment, time-domain detected audio events are revised by considering spectral characteristics of the audio signal in the neighborhood of audio events. A spectrogram may be constructed for the analyzed audio signal, and a 2-D diamond-shaped time-frequency area filtering process may be performed to detect and extract pronounced spectral magnitude peaks. A spectral event vector may be populated with magnitude and time-frequency coordinates for each selected peak.
In at least one embodiment, one or more spectrogram time-spread range(s) are constructed around audio event time positions obtained in the time-domain analysis. By counting and recording spectral event vector peaks in a particular time spread range, an audio event qualifier may be established for each time-domain detected audio event. In at least one embodiment, audio event time positions having an audio event qualifier value below a certain threshold are accepted as viable audio event points, and any remaining audio event time positions are suppressed. In general, qualification of the time-domain detected audio events can be performed based on spectral analysis of each individual time range around a detected audio event, or it can be based on a spectral analysis of a combination of time ranges around a detected audio event.
In at least one embodiment, the spectrogram-based revised (qualified) audio event time positions are processed by considering a schedule of minimal time distances between consecutive audio events, and by subsequent removal of undesirable, redundant audio events, to obtain a final desired audio event timeline for the game.
In various embodiments, any or all of the above-described techniques can be applied singly or in any suitable combination.
In various embodiments, a method for identifying a boundary of a highlight may include some or all of the following steps:
In addition, initial pre-processing of decoded audio stream can be performed for at least one of noise reduction, click removal, and audience noise reduction, with a choice of interchangeable digital filtering stages.
In at least one embodiment, independent pre-processing may be performed to analyze the audio signal in the time domain and/or the frequency domain. Audio signal analysis may be performed in the time domain for generating initial indicators of occurrences of distinct high-energy audio events. An analyzing time window size may be selected together with a size of an analysis window overlap region. The analyzing time window may be advanced along the audio signal. At each window position, a normalized magnitude for window samples may be computed, followed by expansion to full-scale dynamic range.
An average sample magnitude may be calculated for the analysis window, and a log magnitude indicator may be generated at each analysis window position. A time-domain event vector may be populated with computed pairs of analysis window indicator and associated position.
A spectrogram may be constructed for the analysis of audio signal in the frequency domain. A 2-D diamond-shaped spectrogram area filter may be constructed for detection and selection of pronounced time-frequency magnitude peaks. The area filter may be advanced along the time and frequency spectrogram axes, and at each time-frequency position, an area filter central peak magnitude may be checked against all remaining peak magnitudes. In at least one embodiment, the area filter central peak magnitude is retained only if it is greater than all other area filter peak magnitudes. The spectral event vector may be populated with all retained area filter central peak magnitudes.
A joint analysis of audio events detected in the time domain and in the time-frequency domain may be performed. A spectrogram time-spread range around selected time-domain audio events may be determined, and may be used for audio event qualifier computation. Spectral event vector elements positioned in the spectrogram time-spread range at time-domain detected points may be counted and recorded as qualifiers for time-domain detected audio event. The qualifier associated with each time-domain detected audio event may be compared against a threshold, and all time-domain detected audio events with a qualifier above the threshold may be suppressed.
A qualifier revised event vector may be generated. The qualifier revised event vector may further be processed according to a schedule of minimal time distances between adjacent audio events. By subsequent suppression of undesirable, redundant audio events, a final desired audio event timeline for the game may be obtained. The audio event information may further be processed and automatically appended to metadata associated with the sporting event television programming highlights.
According to various embodiments, the system can be implemented on any electronic device, or set of electronic devices, equipped to receive, store, and present information. Such an electronic device may be, for example, a desktop computer, laptop computer, television, smartphone, tablet, music player, audio device, kiosk, set-top box (STB), game system, wearable device, consumer electronic device, and/or the like.
Although the system is described herein in connection with an implementation in particular types of computing devices, one skilled in the art will recognize that the techniques described herein can be implemented in other contexts, and indeed in any suitable device capable of receiving and/or processing user input, and presenting output to the user. Accordingly, the following description is intended to illustrate various embodiments by way of example, rather than to limit scope.
Referring now to
Client device 106 can be any electronic device, such as a desktop computer, laptop computer, television, smartphone, tablet, music player, audio device, kiosk, set-top box, game system, wearable device, consumer electronic device, and/or the like. In at least one embodiment, client device 106 has a number of hardware components well known to those skilled in the art. Input device(s) 151 can be any component(s) that receive input from user 150, including, for example, a handheld remote control, keyboard, mouse, stylus, touch-sensitive screen (touchscreen), touchpad, gesture receptor, trackball, accelerometer, five-way switch, microphone, or the like. Input can be provided via any suitable mode, including for example, one or more of: pointing, tapping, typing, dragging, gesturing, tilting, shaking, and/or speech. Display screen 152 can be any component that graphically displays information, video, content, and/or the like, including depictions of events, highlights, and/or the like. Such output may also include, for example, audiovisual content, data visualizations, navigational elements, graphical elements, queries requesting information and/or parameters for selection of content, metadata, and/or the like. In at least one embodiment, where only some of the desired output is presented at a time, a dynamic control, such as a scrolling mechanism, may be available via input device(s) 151 to choose which information is currently displayed, and/or to alter the manner in which the information is displayed.
Processor 157 can be a conventional microprocessor for performing operations on data under the direction of software, according to well-known techniques. Memory 156 can be random-access memory, having a structure and architecture as are known in the art, for use by processor 157 in the course of running software for performing the operations described herein. Client device 106 can also include local storage (not shown), which may be a hard drive, flash drive, optical or magnetic storage device, web-based (cloud-based) storage, and/or the like.
Any suitable type of communications network 104, such as the Internet, a television network, a cable network, a cellular network, and/or the like can be used as the mechanism for transmitting data between client device 106 and various server(s) 102, 114, 116 and/or content provider(s) 124 and/or data provider(s) 122, according to any suitable protocols and techniques. In addition to the Internet, other examples include cellular telephone networks, EDGE, 3G, 4G, long term evolution (LTE), Session Initiation Protocol (SIP), Short Message Peer-to-Peer protocol (SMPP), SS7, Wi-Fi, Bluetooth, ZigBee, Hypertext Transfer Protocol (HTTP), Secure Hypertext Transfer Protocol (SHTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), and/or the like, and/or any combination thereof. In at least one embodiment, client device 106 transmits requests for data and/or content via communications network 104, and receives responses from server(s) 102, 114, 116 containing the requested data and/or content.
In at least one embodiment, the system of
In at least one embodiment, system 100 identifies highlights of audiovisual content depicting an event, such as a broadcast of a sporting event, by analyzing audio content representing the event. This analysis may be carried out in real-time. In at least one embodiment, system 100 includes one or more web server(s) 102 coupled via a communications network 104 to one or more client devices 106. Communications network 104 may be a public network, a private network, or a combination of public and private networks such as the Internet. Communications network 104 can be a LAN, WAN, wired, wireless and/or combination of the above. Client device 106 is, in at least one embodiment, capable of connecting to communications network 104, either via a wired or wireless connection. In at least one embodiment, client device may also include a recording device capable of receiving and recording events, such as a DVR, PVR, or other media recording device. Such recording device can be part of client device 106, or can be external; in other embodiments, such recording device can be omitted. Although
Web server(s) 102 may include one or more physical computing devices and/or software that can receive requests from client device(s) 106 and respond to those requests with data, as well as send out unsolicited alerts and other messages. Web server(s) 102 may employ various strategies for fault tolerance and scalability such as load balancing, caching and clustering. In at least one embodiment, web server(s) 102 may include caching technology, as known in the art, for storing client requests and information related to events.
Web server(s) 102 may maintain, or otherwise designate, one or more application server(s) 114 to respond to requests received from client device(s) 106. In at least one embodiment, application server(s) 114 provide access to business logic for use by client application programs in client device(s) 106. Application server(s) 114 may be co-located, co-owned, or co-managed with web server(s) 102. Application server(s) 114 may also be remote from web server(s) 102. In at least one embodiment, application server(s) 114 interact with one or more analytical server(s) 116 and one or more data server(s) 118 to perform one or more operations of the disclosed technology.
One or more storage devices 153 may act as a “data store” by storing data pertinent to operation of system 100. This data may include, for example, and not by way of limitation, audio data 154 representing one or more audio signals. Audio data 154 may, for example, be extracted from audiovisual streams or stored audiovisual content representing sporting events and/or other events.
Audio data 154 can include any information related to audio embedded in the audiovisual stream, such as an audio stream that accompanies video imagery, processed versions of the audiovisual stream, and metrics and/or vectors related to audio data 154, such as time indices, durations, magnitudes, and/or other parameters of events. User data 155 can include any information describing one or more users 150, including for example, demographics, purchasing behavior, audiovisual stream viewing behavior, interests, preferences, and/or the like. Highlight data 164 may include highlights, highlight identifiers, time indicators, categories, excitement levels, and other data pertaining to highlights. Audio data 154, user data 155, and highlight data 164 will be described in detail subsequently.
Notably, many components of system 100 may be, or may include, computing devices. Such computing devices may each have an architecture similar to that of client device 106, as shown and described above. Thus, any of communications network 104, web servers 102, application servers 114, analytical servers 116, data providers 122, content providers 124, data servers 118, and storage devices 153 may include one or more computing devices, each of which may optionally have an input device 151, display screen 152, memory 156, and/or a processor 157, as described above in connection with client devices 106.
In an exemplary operation of system 100, one or more users 150 of client devices 106 view content from content providers 124, in the form of audiovisual streams. The audiovisual streams may show events, such as sporting events. The audiovisual streams may be digital audiovisual streams that can readily be processed with known computer vision techniques.
As the audiovisual streams are displayed, one or more components of system 100, such as client devices 106, web servers 102, application servers 114, and/or analytical servers 116, may analyze the audiovisual streams, identify highlights within the audiovisual streams, and/or extract metadata from the audiovisual stream, for example, from an audio component of the stream. This analysis may be carried out in response to receipt of a request to identify highlights and/or metadata for the audiovisual stream. Alternatively, in another embodiment, highlights and/or metadata may be identified without a specific request having been made by user 150. In yet another embodiment, the analysis of audiovisual streams can take place without an audiovisual stream being displayed.
In at least one embodiment, user 150 can specify, via input device(s) 151 at client device 106, certain parameters for analysis of audio data 154 (such as, for example, what event/games/teams to include, how much time user 150 has available to view the highlights, what metadata is desired, and/or any other parameters). User preferences can also be extracted from storage, such as from user data 155 stored in one or more storage devices 153, so as to customize analysis of audio data 154 without necessarily requiring user 150 to specify preferences. In at least one embodiment, user preferences can be determined based on observed behavior and actions of user 150, for example, by observing website visitation patterns, television watching patterns, music listening patterns, online purchases, previous highlight identification parameters, highlights and/or metadata actually viewed by user 150, and/or the like.
Additionally, or alternatively, user preferences can be retrieved from previously stored preferences that were explicitly provided by user 150. Such user preferences may indicate which teams, sports, players, and/or types of events are of interest to user 150, and/or they may indicate what type of metadata or other information related to highlights, would be of interest to user 150. Such preferences can therefore be used to guide analysis of the audiovisual stream to identify highlights and/or extract metadata for the highlights.
Analytical server(s) 116, which may include one or more computing devices as described above, may analyze live and/or recorded feeds of play-by-play statistics related to one or more events from data provider(s) 122. Examples of data provider(s) 122 may include, but are not limited to, providers of real-time sports information such as STATS™, Perform (available from Opta Sports of London, UK), and SportRadar of St. Gallen, Switzerland. In at least one embodiment, analytical server(s) 116 generate different sets of excitement levels for events; such excitement levels can then be stored in conjunction with highlights identified by or received by system 100 according to the techniques described herein.
Application server(s) 114 may analyze the audiovisual stream to identify the highlights and/or extract the metadata. Additionally, or alternatively, such analysis may be carried out by client device(s) 106. The identified highlights and/or extracted metadata may be specific to a user 150; in such case, it may be advantageous to identify the highlights in client device 106 pertaining to a particular user 150. Client device 106 may receive, retain, and/or retrieve the applicable user preferences for highlight identification and/or metadata extraction, as described above. Additionally, or alternatively, highlight generation and/or metadata extraction may be carried out globally (i.e., using objective criteria applicable to the user population in general, without regard to preferences for a particular user 150). In such a case, it may be advantageous to identify the highlights and/or extract the metadata in application server(s) 114.
Content that facilitates highlight identification, audio analysis, and/or metadata extraction may come from any suitable source, including from content provider(s) 124, which may include websites such as YouTube, MLB.com, and the like; sports data providers; television stations; client- or server-based DVRs; and/or the like. Alternatively, content can come from a local source such as a DVR or other recording device associated with (or built into) client device 106. In at least one embodiment, application server(s) 114 generate a customized highlight show, with highlights and metadata, available to user 150, either as a download, or streaming content, or on-demand content, or in some other manner.
As mentioned above, it may be advantageous for user-specific highlight identification, audio analysis, and/or metadata extraction to be carried out at a particular client device 106 associated with a particular user 150. Such an embodiment may avoid the need for video content or other high-bandwidth content to be transmitted via communications network 104 unnecessarily, particularly if such content is already available at client device 106.
For example, referring now to
Returning to
Additional details on such functionality are provided in the above-cited related U.S. patent applications.
In at least one embodiment, one or more data server(s) 118 are provided. Data server(s) 118 may respond to requests for data from any of server(s) 102, 114, 116, for example to obtain or provide audio data 154, user data 155, and/or highlight data 164. In at least one embodiment, such information can be stored at any suitable storage device 153 accessible by data server 118, and can come from any suitable source, such as from client device 106 itself, content provider(s) 124, data provider(s) 122, and/or the like.
Referring now to
User data 155 may include preferences and interests of user 150. Based on such user data 155, system 180 may extract highlights and/or metadata to present to user 150 in the manner described herein. Additionally, or alternatively, highlights and/or metadata may be extracted based on objective criteria that are not based on information specific to user 150.
Referring now to
The specific hardware architectures depicted in
In at least one embodiment, the system can be implemented as software written in any suitable computer programming language, whether in a standalone or client/server architecture. Alternatively, it may be implemented and/or embedded in hardware.
As shown, audio data 154 may include a record for each of a plurality of audio streams 200. For illustrative purposes, audio streams 200 are depicted, although the techniques described herein can be applied to any type of audio data 154 or content, whether streamed or stored. The records of audio data 154 may include, in addition to the audio streams 200, other data produced pursuant to, or helpful for, analysis of the audio streams 200. For example, audio data 154 may include, for each audio stream 200, a spectrogram 202, one or more analysis windows 204, vectors 206, and time indices 208.
Each audio stream 200 may reside in the time domain. Each spectrogram 202 may be computed for the corresponding audio stream 200 in the time-frequency domain. Spectrogram 202 may be analyzed to more easily locate audio events.
Analysis windows 204 may be designations of predetermined time and/or frequency intervals of the spectrograms 202. Computationally, a single moving (i.e., “sliding”) analysis window 204 may be used to analyze a spectrogram 202, or a series of displaced (optionally overlapping) analysis windows 204 may be used.
Vectors 206 may be data sets containing interim and/or final results from analysis of audio stream 200 and/or corresponding spectrogram 202.
Time indices 208 may indicate times, within audio stream 200 (and/or the audiovisual stream from which audio stream 200 is extracted) at which key audio events occur. For example, time indices 208 may be the times, within the audiovisual content, at which the audio events begin, are centered, or end. Thus, time indices 208 may indicate the beginnings or ends of particularly interesting parts of the audiovisual stream, such as, in the context of a sporting event, important or impressive plays, or plays that may be of particular interest to a particular user 150.
As further shown, user data 155 may include records pertaining to users 150, each of which may include demographic data 212, preferences 214, viewing history 216, and purchase history 218 for a particular user 150.
Demographic data 212 may include any type of demographic data, including but not limited to age, gender, location, nationality, religious affiliation, education level, and/or the like.
Preferences 214 may include selections made by user 150 regarding his or her preferences. Preferences 214 may relate directly to highlight and metadata gathering and/or viewing, or may be more general in nature. In either case, preferences 214 may be used to facilitate identification and/or presentation of the highlights and metadata to user 150.
Viewing history 216 may list television programs, audiovisual streams, highlights, web pages, search queries, sporting events, and/or other content retrieved and/or viewed by user 150.
Purchase history 218 may list products or services purchased or requested by user 150.
As further shown, highlight data 164 may include records for j highlights 220, each of which may include an audiovisual stream 222 and/or metadata 224 for a particular highlight 220.
Audiovisual stream 222 may include audio and/or video depicting highlight 220, which may be obtained from one or more audiovisual streams of one or more events (for example, by cropping the audiovisual stream to include only audiovisual stream 222 pertaining to highlight 220). Within metadata 224, identifier 223 may include time indices (such as time indices 208 of audio data 154) and/or other indicia that indicate where highlight 220 resides within the audiovisual stream of the event from which it is obtained.
In some embodiments, the record for each of highlights 220 may contain only one of audiovisual stream 222 and identifier 223. Highlight playback may be carried out by playing audiovisual stream 222 for user 150, or by using identifier 223 to play only the highlighted portion of the audiovisual stream for the event from which highlight 220 is obtained. Storage of identifier 223 is optional; in some embodiments, identifier 223 may only be used to extract audiovisual stream 222 for highlight 220, which may then be stored in place of identifier 223. In either case, time indices 208 for highlight 220 may be extracted from audio data 154 and stored, at least temporarily, as metadata 224 that is either appended to highlight 220, or to the audiovisual stream from which audio data 154 and highlight 220 are obtained. In some embodiments, time indices 208 may be stored as boundaries 232 of identifier 223.
In addition to or in the alternative to identifier 223, metadata 224 may include information about highlight 220, such as the event date, season, and groups or individuals involved in the event or the audiovisual stream from which highlight 220 was obtained, such as teams, players, coaches, anchors, broadcasters, and fans, and/or the like. Among other information, metadata 224 for each highlight 220 may include a phase 226, clock 227, score 228, a frame number 229, and/or an excitement level 230.
Phase 226 may be the phase of the event pertaining to highlight 220. More particularly, phase 226 may be the stage of a sporting event in which the start, middle, and/or end of highlight 220 resides. For example, phase 226 may be “third quarter,” “second inning,” “bottom half,” or the like.
Clock 227 may be the game clock pertaining to highlight 220. More particularly, clock 227 may be the state of the game clock at the start, middle, and/or end of highlight 220. For example, clock 227 may be “15:47” for a highlight 220 that begins, ends, or straddles the period of a sporting event at which fifteen minutes and forty-seven seconds are displayed on the game clock.
Score 228 may be the game score pertaining to highlight 220. More particularly, score 228 may be the score at the beginning, end, and/or middle of highlight 220. For example, score 228 may be “45-38,” “7-0,” “30-love,” or the like.
Frame number 229 may be the number of the video frame, within the audiovisual stream from which highlight 220 is obtained, or audiovisual stream 222 pertaining to highlight 220, that relates to the start, middle, and/or end of highlight 220.
Excitement level 230 may be a measure of how exciting or interesting an event or highlight is expected to be for a particular user 150, or for users in general. In at least one embodiment, excitement level 230 may be computed as indicated in the above-referenced related applications. Additionally, or alternatively, excitement level 230 may be determined, at least in part, by analysis of audio data 154, which may be a component that is extracted from audiovisual stream 222 and/or audio stream 200. For example, audio data 154 that contains higher levels of crowd noise, announcements, and/or up-tempo music may be indicative of a high excitement level 230 for associated highlight 220. Excitement level 230 need not be static for a highlight 220, but may instead change over the course of highlight 220. Thus, system 100 may be able to further refine highlights 220 to show a user only portions that are above a threshold excitement level 230.
The data structures set forth in
In at least one embodiment, the system performs several stages of analysis of audio data 154 in both the time and time-frequency domains, so as to detect bursts of energy (i.e., audio volume) due to occurrences during an audiovisual program, such as a broadcast of a sporting event. One example of such a burst of high-energy audio is a tennis ball hit during the delivery of a tennis serve.
First, a compressed audio signal may be read, decoded, and resampled to a desired sampling rate. Next, a resulting PCM audio signal may be pre-filtered for noise reduction, click removal, and/or audience noise reduction, using any of a number of interchangeable digital filtering stages.
Subsequently, time-domain analysis may be performed on the audio data 154, followed by time-frequency spectrogram generation and a joined time-frequency analysis. Audio event detection may be performed in successive stages, with time-domain detection results fed into the spectral neighborhood analysis. Detection of distinct spectral spread in time-frequency at time positions obtained by time-domain analysis may be applied to reduce false positive detections generated by strong audio energy peaking due to audience noise such as clapping and cheering. Finally, two-level filtering with back adjustments of time intervals between desired audio event detections may be applied to an event vector to obtain a final desired audio event timeline for the entire sporting event.
Time indices 208 before and/or after the high-energy audio bursts may be used as boundaries 232 (for example, beginnings or ends) of highlights 220. In some embodiments, these time indices 208 may be used to identify the actual beginning and/or ending points of highlights 220 that have already been identified (for example, with tentative boundaries 232 which may be tentative beginning and ending points that can subsequently be adjusted based on identification of audio events). Highlights 220 may be extracted and/or identified, within the video stream, for subsequent viewing by the user.
In at least one embodiment, method 400 (and/or other methods described herein) is performed on audio data 154 that has been extracted from audiovisual stream or other audiovisual content. Alternatively, the techniques described herein can be applied to other types of source content. For example, audio data 154 need not be extracted from an audiovisual stream; rather it may be a radio broadcast or other audio depiction of a sporting event or other event.
In at least one embodiment, method 400 (and/or other methods described herein) may be performed by a system such as system 100 of
Method 400 of
In a step 430, audio data 154 may be filtered using any of a number of interchangeable digital filtering stages. Digital filtering of decoded audio data 154 may be different for time-domain analysis as compared to digital filtering for the frequency-domain analysis; accordingly, in at least one embodiment, two lines of filter stages are formed and the results are routed to two independent PCM buffers, one for each domain of processing.
Next, in a step 440, an array of spectrograms 202 may be generated for the filtered audio data 154, for example by computing a Short-time Fourier Transform (STFT) on one-second chunks of the filtered audio data 154. Time-frequency coefficients each for spectrogram 202 may be saved in a two-dimensional array for further processing.
In some embodiments, when the desired audio events 320 can be identified without spectral content, step 440 may be omitted, and further analysis may be simplified by performing such analysis on time-domain audio data 154 only. However, in such a case, undesirable audio event 320 detections may occur due to inherently unreliable indicators based on thresholding of audio volume only, without consideration of spectral content pertinent to particular sounds of interest such as a commentator's voice and/or background audience noise; such sounds may be of low volume in the time domain but may have rich spectral content in the time-frequency domain. Thus, as described below, it can be beneficial to perform analysis of the audio stream in both the time domain and time-frequency domain, with subsequent consolidation of detected audio events into a final result.
Accordingly, in further descriptions in connection with
The method 500 may proceed to a step 520 in which analysis window 204 slides along the audio data 154 in successive steps S along time axes of the audio data 154. In a step 530, at each position of analysis window 204, a normalized magnitude for audio samples is computed. The normalized magnitudes may be expanded to a full-scale dynamic range. In a step 540, an average sample magnitude is calculated for the analysis window, and a log magnitude indicator is generated at each window position. In a step 550, a time event vector may be populated with detected time-domain audio events described by pairs of magnitude-indicator and associated time-position. This time-domain event vector may subsequently be used in an audio event evaluation/revision process invoking audio signal spectral characteristics in the neighborhood of detected audio events.
As mentioned previously, in some embodiments, a spectrogram 202 is constructed for the analyzed audio data 154. In at least one embodiment, 2-D diamond-shaped time-frequency area filtering may be performed to extract pronounced spectral magnitude peaks. A spectral event vector may be populated with magnitude and time-frequency coordinates for each selected peak. Furthermore, a spectrogram time spread range may be constructed around audio event time positions obtained in the above-described time-domain analysis, and selected spectrogram magnitude peaks in this time spread range may be counted and recorded. In this manner, a qualifier may be established for each point in the time-domain events vector. Only audio event time positions with the qualifier below a certain threshold may be accepted as viable audio event points.
Once all positions of the 2D diamond-shaped area filter have been analyzed, the method 600 may end, and further processing may be taken in subsequent methods (for example, the method 700 of
In a step 740, spectral event vector elements positioned in the spectrogram time spread range around selected time-domain audio events may be counted and recorded as qualifiers for each audio event. In a query 750, the qualifier associated with each time-domain audio event may be compared against a threshold. In a step 760, all audio events with a qualifier below the threshold may be accepted. Conversely, in a step 770, all audio events with a qualifier above the threshold may be suppressed. Step 770 may remove most of the dense bursts of high-energy audio events with pronounced spectral peaks extending over the entire spectrogram time spread, thus reducing the incidence of false detection of the desired occurrence. For example, step 770 may reduce the likelihood of false tennis serve detection due to audience clapping, chanting, loud music, etc.
In a query 780, method 700 may determine whether the end of the time event vector has been reached. If not, method 700 may return to step 730 and advance to the next position in the time event vector. If the end of the time event vector has been reached, method 700 may proceed to a step 790 in which a qualifier revised event vector is generated. Processing may then proceed to further audio event selection in accordance to a desired audio event spacing schedule, as will be set forth in method 800 of
In at least one embodiment, this further processing of the qualified events vector removes audio events in close proximity to one another that may be redundant and undesirable. In the exemplary case of tennis games, these redundant audio events may be due to a series of densely spaced tennis ball bounces before a serve is delivered. Hence, the qualified audio events may be subjected to a schedule of minimal allowed time distances between consecutive audio events. Thus, method 800 of
Method 800 may be repeated as desired with adjusted time distance thresholding.
The event vector post-processing steps as described above may be performed in any desired order. The depicted steps can be performed in any combination with one another, and some steps can be omitted. At the end of the process (i.e., when the end of the event vector has been reached), a new final event vector may be generated containing a desired audio event timeline for the game. Optionally, the audio events may further be elaborated on with crowd noise detection, announcer voice recognition, and the like in order to further refine identification of the audio events.
In at least one embodiment, the automated video highlights and associated metadata generation application receives a live broadcast program, or a digital audiovisual stream via a computer server, and processes audio data 154 using digital signal processing techniques so as to detect high-energy audio associated with, for example, tennis ball hits and related tennis serve delivery in tennis games, as described above. These audio events may be sorted and selected using the techniques described herein. Extracted information may then be appended to metadata 224 associated with an event, such as a sporting event. Metadata 224 may be associated with the event television programming video highlights, and can be used, for example, to determine boundaries 232 (i.e., start and/or end times) for segments used in highlight generation.
For example, the start of a highlight may be established ten seconds prior to an audio event identified as a tennis serve. Similarly, the end of the highlight may be established ten seconds prior to the next audio event identified as a tennis serve. Thus, one volley of the game may be isolated in a highlight. Of course, boundaries 232 may be identified in many other ways through the techniques used to analyze audio data 154, as presented herein.
The present system and method have been described in particular detail with respect to possible embodiments. Those of skill in the art will appreciate that the system and method may be practiced in other embodiments. First, the particular naming of the components, capitalization of terms, the attributes, data structures, or any other programming or structural aspect is not mandatory or significant, and the mechanisms and/or features may have different names, formats, or protocols. Further, the system may be implemented via a combination of hardware and software, or entirely in hardware elements, or entirely in software elements. Also, the particular division of functionality between the various system components described herein is merely exemplary, and not mandatory; functions performed by a single system component may instead be performed by multiple components, and functions performed by multiple components may instead be performed by a single component.
Reference in the specification to “one embodiment”, or to “an embodiment”, means that a particular feature, structure, or characteristic described in connection with the embodiments is included in at least one embodiment. The appearances of the phrases “in one embodiment” or “in at least one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.
Various embodiments may include any number of systems and/or methods for performing the above-described techniques, either singly or in any combination. Another embodiment includes a computer program product comprising a non-transitory computer-readable storage medium and computer program code, encoded on the medium, for causing a processor in a computing device or other electronic device to perform the above-described techniques.
Some portions of the above are presented in terms of algorithms and symbolic representations of operations on data bits within the memory of a computing device. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps (instructions) leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical, magnetic or optical signals capable of being stored, transferred, combined, compared and otherwise manipulated. It is convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like. Furthermore, it is also convenient at times, to refer to certain arrangements of steps requiring physical manipulations of physical quantities as modules or code devices, without loss of generality.
It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussion, it is appreciated that throughout the description, discussions utilizing terms such as “processing” or “computing” or “calculating” or “displaying” or “determining” or the like, refer to the action and processes of a computer system, or similar electronic computing module and/or device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system memories or registers or other such information storage, transmission or display devices.
Certain aspects include process steps and instructions described herein in the form of an algorithm. It should be noted that the process steps and instructions can be embodied in software, firmware and/or hardware, and when embodied in software, can be downloaded to reside on and be operated from different platforms used by a variety of operating systems.
The present document also relates to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computing device. Such a computer program may be stored in a computer readable storage medium, such as, but not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, DVD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, flash memory, solid state drives, magnetic or optical cards, application specific integrated circuits (ASICs), or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus. The program and its associated data may also be hosted and run remotely, for example on a server. Further, the computing devices referred to herein may include a single processor or may be architectures employing multiple processor designs for increased computing capability.
The algorithms and displays presented herein are not inherently related to any particular computing device, virtualized system, or other apparatus. Various general-purpose systems may also be used with programs in accordance with the teachings herein, or it may be more convenient to construct specialized apparatus to perform the required method steps. The required structure for a variety of these systems will be apparent from the description provided herein. In addition, the system and method are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings described herein, and any references above to specific languages are provided for disclosure of enablement and best mode.
Accordingly, various embodiments include software, hardware, and/or other elements for controlling a computer system, computing device, or other electronic device, or any combination or plurality thereof. Such an electronic device can include, for example, a processor, an input device (such as a keyboard, mouse, touchpad, track pad, joystick, trackball, microphone, and/or any combination thereof), an output device (such as a screen, speaker, and/or the like), memory, long-term storage (such as magnetic storage, optical storage, and/or the like), and/or network connectivity, according to techniques that are well known in the art. Such an electronic device may be portable or non-portable. Examples of electronic devices that may be used for implementing the described system and method include: a desktop computer, laptop computer, television, smartphone, tablet, music player, audio device, kiosk, set-top box, game system, wearable device, consumer electronic device, server computer, and/or the like. An electronic device may use any operating system such as, for example and without limitation: Linux; Microsoft Windows, available from Microsoft Corporation of Redmond, Wash.; Mac OS X, available from Apple Inc. of Cupertino, Calif.; iOS, available from Apple Inc. of Cupertino, Calif.; Android, available from Google, Inc. of Mountain View, Calif.; and/or any other operating system that is adapted for use on the device.
While a limited number of embodiments have been described herein, those skilled in the art, having benefit of the above description, will appreciate that other embodiments may be devised. In addition, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the subject matter. Accordingly, the disclosure is intended to be illustrative, but not limiting, of scope.
The present application is a continuation of U.S. application Ser. No. 16/553,025, filed Aug. 27, 2019, which is a continuation-in-part of U.S. application Ser. No. 16/440,229, filed Jun. 13, 2019, and a continuation-in-part of U.S. application Ser. No. 16/421,391, filed May 23, 2019. U.S. application Ser. No. 16/440,229, filed Jun. 13, 2019, claims the benefit of priority to U.S. Provisional Ser. No. 62/712,041, filed Jul. 30, 2018, and U.S. Provisional Ser. No. 62/746,454, filed Oct. 16, 2018. U.S. application Ser. No. 16/421,391, filed May 23, 2019, claims the benefit of U.S. Provisional Ser. No. 62/680,955, filed Jun. 5, 2018; U.S. Provisional Ser. No. 62/712,041, filed Jul. 30, 2018; and U.S. Provisional Ser. No. 62/746,454, filed Oct. 16, 2018. The present application is also related to U.S. application Ser. No. 13/601,915, filed Aug. 31, 2012 and issued on Jun. 16, 2015 as U.S. Pat. No. 9,060,210; U.S. application Ser. No. 13/601,927, filed Aug. 31, 2012 and issued on Sep. 23, 2014 as U.S. Pat. No. 8,842,007; U.S. application Ser. No. 13/601,933, filed Aug. 31, 2012 and issued on Nov. 26, 2013 as U.S. Pat. No. 8,595,763; U.S. application Ser. No. 14/510,481, filed Oct. 9, 2014; U.S. application Ser. No. 14/710,438, filed May 12, 2015; U.S. application Ser. No. 14/877,691, filed Oct. 7, 2015; U.S. application Ser. No. 15/264,928, filed Sep. 14, 2016; U.S. application Ser. No. 16/411,704, filed May 14, 2019; U.S. application Ser. No. 16/411,710, filed May 14, 2019; U.S. application Ser. No. 16/411,713, filed May 14, 2019, all of which are incorporated herein by reference in their entirety.
| Number | Date | Country | |
|---|---|---|---|
| 62712041 | Jul 2018 | US | |
| 62746454 | Oct 2018 | US | |
| 62680955 | Jun 2018 | US | |
| 62712041 | Jul 2018 | US | |
| 62746454 | Oct 2018 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 16553025 | Aug 2019 | US |
| Child | 17681115 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 16440229 | Jun 2019 | US |
| Child | 16553025 | US | |
| Parent | 16421391 | May 2019 | US |
| Child | 16553025 | US |
