Model-based media classification service using sensed media noise characteristics

A neural network-based classifier system can receive a query including a media signal and, in response, provide an indication that the query corresponds to a specified media type or media class. The neural network-based classifier system can select and apply various models to facilitate media classification. In an example embodiment, a query can be analyzed for various characteristics, such as a noise profile, before it is input to the network-based classifier. If the query has greater than a specified threshold noise characteristic, then a successful classification can be unlikely and a classification process based on the query can be terminated before computational resources are expended. Query signals that meet or exceed a threshold condition can be provided to the network-based classifier for media classification. In an example embodiment, a remote device or a central media classifier circuit can determine a noise profile for a query.

BACKGROUND

Media content provided by entertainment, news, and other communications sources, is ubiquitous in today's modern society. Even through no effort of their own, individuals are subject to constant advertising in public spaces, or ambient audio or video entertainment in spaces like restaurants and waiting areas. In some cases, individuals receive their own media content from various sources, such as by tuning in to a particular radio or television broadcast, or by receiving media streamed via the internet from social media or other sources.

Portable or fixed electronic devices, such as including cellular telephones, televisions, or tablet computers, can directly or indirectly receive media content. For example, a television can receive streaming audio and/or video content via the Internet in response to a user request for media. In an example, a smart phone can be configured to receive information about ambient media using an on-board microphone or camera. In some examples, a smart phone can be directed by a user to identify a source or other characteristic of a sample of ambient media, such as by using an app to communicate with a third party that provides identification services.

DETAILED DESCRIPTION

In an example embodiment, a machine can be configured to interact with one or more users or user devices to identify audio or video content. One approach is to configure the machine to perform audio fingerprinting, or classification, based on an audio sample, for example, by comparing a query fingerprint of an audio signal sample against various reference audio sample fingerprints in a database in an attempt to identify a match. When a query is performed using large databases, such audio fingerprinting can be computationally expensive and time consuming. Various systems and techniques described herein can be used to reduce the cost and expense of performing classification services, such as by using classification models that are tuned for particular environments, media types, or other scenarios. In an example embodiment, context information about a media query can be used to help select a classification model from among multiple different available classification models. In an example embodiment, some classification models applied by a classifier system, such as a convolutional neural network-based media classifier, can yield media classification results more efficiently than other models.

FIG. 1illustrates generally an example embodiment of a system configured to receive and analyze media information.FIG. 1includes a network diagram illustrating a network environment100suitable for media classification, according to some example embodiments. The network environment100includes a media processor circuit110, a database115, and first and second mobile devices130and150, such as can be communicatively coupled to each other via a network190. The media processor circuit110, with or without the database115, can form part of a cloud118(e.g., an audio processing cloud, such as including multiple geographically distributed machines configured to function independently or collectively as a server to provide media processing services). The cloud118can form all or part of a system that includes a network-based media classifier105, for example, as a cloud-based server system configured to provide network-based audio processing services to the first and second mobile devices130and150. The media processor circuit110and the first and second mobile devices130and150can each be implemented in a special-purpose (e.g., specialized) computer system, in whole or in part, as further described herein.

In an example embodiment, the network environment100is configured to perform classification services on audio or video data. That is, in response to some input media, such as a sample of audio or video information from one of the first and second mobile devices130and150, the various components of the cloud118can be used to provide an indication of a class or classification of the input media. A classification can include some characteristic associated with the media. In an example embodiment where input media includes an audio signal, a classification can include an indication of an author or artist, an album name, a source, a tempo, a mood, or other quantitative or qualitative feature corresponding to the audio signal. In an example embodiment, the input media is at least a portion of a song, and the network-based media classifier105can provide an indication of the song's title, artist, names of various recordings and/or albums associated with the song, or other information about or associated with the song.

In the example embodiment ofFIG. 1, the network-based media classifier105and/or one of the first and second mobile devices130and150are configured to access input media queries from other sources, such as via an intranet or via the Internet. In an example embodiment, the network-based media classifier105receives unclassified media data from a transceiver or receiver that is configured to receive broadcast signals, such as television, radio, or other signals.

A processor circuit can be configured to analyze an input media query to access or identify a context parameter that corresponds to the query. The processor circuit can include the media processor circuit110at the cloud118, or the processor circuit can be implemented at one of the first and second mobile devices130and150. In an example embodiment, the processor circuit can be provided at another portion of the network-based media classifier105, or at another location that is communicatively coupled with the network-based media classifier105and/or the first and second mobile devices130and150. In an example embodiment, the processor circuit configured to identify a context parameter can be the same or different processor circuit than is used to perform audio classification services.

A context parameter can be additionally or alternatively accessed from somewhere other than an input media query. For example, a context parameter can include information about an input media query, such as sensory or environmental information that corresponds (e.g., temporally) with the query. In an example embodiment, a context parameter includes location information that corresponds to a location where a query was sampled (e.g., by the first mobile device130). In an example embodiment, a context parameter includes device identification information or user identification information that corresponds to a device or user that acquires or prepares a query. Other context parameters can additionally or alternatively be used, as further discussed herein.

In an example embodiment, a processor circuit can be configured to determine spectral features of a media query. The spectral features can form all or a part of a fingerprint associated with the query. Based on or using a context parameter, the processor circuit can be configured to select a classification model for use in processing the spectral features to provide an indication of a media type that corresponds to the query. In an example embodiment, the processor circuit (e.g., the media processor circuit110) determines a media type probability index for the query using the selected classification model and the determined spectral features. In an example embodiment, the probability index includes an indication of a likelihood that the media data corresponds to a particular known media, such as a particular song, artist, or album. In an example embodiment, if the probability index exceeds some specified threshold likelihood, then the processor circuit can return a media type indication without the probability index.

Returning to the example embodiment ofFIG. 1, first and second users132and152are illustrated. One or both of the first and second users132and152can be a human user (e.g., a human being), a machine user (e.g., a computer configured by a software program to interact with the first or second mobile device130or150), or any suitable combination thereof (e.g., a human assisted by a machine or a machine supervised by a human). The first user132is associated with the first mobile device130and can be a user of the first mobile device130. For example, the first mobile device130can be a computer, a vehicle computer, a tablet computer, a navigational device, a portable media device, a smart phone, or a wearable device (e.g., a smart watch, smart glasses, smart clothing, or smart jewelry) belonging to or having user profile data associated with the first user132. Likewise, the second user152is associated with the second mobile device150and can be a user of the second mobile device150. In an example embodiment, each of the first and second users132and152and/or the first and second mobile devices130and150can be associated with various different classification models for use by the network-based media classifier105.

In an example embodiment, one or both of the first and second mobile devices130and150can be configured to receive unclassified media using a microphone or other media sensor input. In an example embodiment, unclassified media can be sampled from a device's native source (e.g., a sound card or other native component of the first mobile device130or second mobile device150) or from a device output terminal (e.g., a video out, a line out, a headphone jack, etc.) of the first or second mobile devices130or150, or from some other device. The unclassified media can be sampled (e.g., by circuitry on-board the first or second mobile device130or150) and communicated to the network-based media classifier105with a request for media identification services.

Any of the systems, machines, devices, or associated databases shown or described in connection withFIG. 1can be, include, or can otherwise be implemented using a special-purpose (e.g., specialized or otherwise non-generic) computer that has been modified (e.g., configured or programmed by software, such as one or more software modules of an application, operating system, firmware, middleware, or other program) to perform one or more of the functions described herein for that system or machine. An example embodiment of a special-purpose computer system configured to implement any one or more of the methodologies described herein is discussed below atFIG. 12. Within the technical field of such special-purpose computers, a special-purpose computer that has been modified by the structures discussed herein to perform the functions discussed herein is technically improved compared to other special-purpose computers that lack the structures discussed herein or are otherwise unable to perform the functions discussed herein. Accordingly, a special-purpose machine configured according to the systems and methods discussed herein provides an improvement to the technology of similar special-purpose machines.

The database115, which is further discussed below at least atFIG. 6, can include a data storage resource that can be configured to store data in various ways, for example in a text file, a table, a spreadsheet, a relational database (e.g., an object-relational database), a triple store, a hierarchical data store, or any suitable combination thereof. The network190can be any network that enables communication between or among systems, machines, databases, and devices (e.g., between the media processor circuit110and the first mobile device130). The network190can be a wired network, a wireless network (e.g., a mobile or cellular network), or a combination thereof. The network190can include one or more portions that constitute a private network, a public network (e.g., the Internet), or a combination thereof.

The network190can include one or more portions that incorporate a local area network (LAN), a wide area network (WAN), the Internet, a mobile telephone network (e.g., a cellular network), a wired telephone network (e.g., a plain old telephone system (POTS) network), a wireless data network (e.g., a WiFi network or WiMax network), or a combination thereof. Any one or more portions of the network190can communicate information via a transmission medium. As used herein, “transmission medium” refers to any intangible (e.g., transitory) medium that is capable of communicating (e.g., transmitting) instructions for execution by a machine (e.g., by one or more processors of such a machine), and includes digital or analog communication signals or other intangible media to facilitate communication of such software.

FIG. 2illustrates generally a flow diagram of a method that can include providing a media classification. The example embodiment inFIG. 2illustrates generally a flow diagram of a method200for determining a media classification for media data201, for example, using the network environment100. The media data201can be a media query received from one of the first and second mobile devices130and150, or from another source. The method200includes, at operation202, acquiring media training data and, at operation204, analyzing the acquired media training data. The method200further includes, at operation206, classifying media data201(e.g., a media query that includes audio and/or video information) using various training data. At operation210, the method200includes providing an indication of a media classification, or providing an indication of a likelihood that the media data201corresponds to some specified media.

In an example embodiment, the method200includes, at operation206, using a context parameter203. At operation206, the media data201and the context parameter203can be used together to classify the media data using various training data.

In the method200, features can be extracted from media data (e.g., training data or query data) using various digital signal processing (DSP) methods. For example, mel-frequency cepstral coefficients (MFCC), spectral flatness, mean value, zero crossings, spectral centroid, chord class, or percussiveness, among other extractable features, can be used. Various digital signal processing feature extraction methods and standards (e.g. MPEG-7) known in the art can be used. The extracted features can be used to perform comparative analyses of the media data, for example, using a trained classifier at step206. In an example embodiment, training data is used to establish reference standards or models relative to identified criteria, and those models can then be used for evaluating the media data201.

In the example embodiment ofFIG. 2, a training set of media data acquired at step202includes one or more audio signal samples (e.g. a recording of a musical composition) and one or more editorially formed characteristics corresponding to each of the one or more audio signal samples. In an example embodiment, several characteristics can be considered together to be a media profile or fingerprint corresponding to the training set of media data. In an example embodiment, characteristics for the training data are defined automatically by associating media data with other existing media information (e.g., using the Compact Disc Database, CDDB, or other similar resource). In an example embodiment, characteristics for the training data are defined by one or more persons or aggregated from many persons who evaluate media for inclusion in the database115. In an example embodiment, characteristics for the training data are generated using various analyses of aggregated end-user and/or editorially-generated tags or prose associated with the content, or other data mining methods can be applied to datasets whose origins can be independent of the training process.

At operation204, analyzing media from the training data can include extracting various spectral features from the training set of media data. In an example embodiment, extracted features are evaluated against and correlated with the editorially assigned categories to create ‘n’ number of classification models, such as including at least one model for each of the ‘n’ categories. Accordingly, in this example, each classification model can be a correlation set of a category with extracted features typical of that category. Such classification models can be shared with a classifier (e.g., implemented by the media processor circuit110) and applied at operation206, where one or more classification models can serve as a basis for a comparison or reference standard for classifying the media data201.

At operation206, classifying the media data201using the training data can include extracting various spectral features from the query media data201. The extracted features can then be compared with or processed using the classification models from204. Classifying media at operation206can include, at operation216, transforming the media data201, or at operation226, applying one or more classification models. In an example embodiment, classifying the media data201can include using a convolutional neural network trained on the previously analyzed training data. The convolutional neural network can be a processor-implemented classifier that uses media signal features from media data201that is sampled over one or more time windows (e.g., several milliseconds to several seconds). The classifier can be configured to detect whether a signal event or spectral feature is present in the media data201or not. The convolutional neural network generally contains thousands to millions of parameters that can be tuned for specific media events (e.g. whistles, dog barks, presence of music), and situations (e.g. specific recording device/microphone, environmental noises).

At operation216, various functions can be applied to the media data201. For example, a constant Q transform can be applied to convert a data series from the media data201to a frequency domain. Various spectral features can be identified in the media data201to discern different media types. Local contrast normalization can be performed, and then the resulting data can be applied to the convolutional neural network. When the media data201includes an audio signal, the local contrast normalization process can help make classifications invariant to overall volume changes in the audio signal and, to some degree, invariant to masking by extraneous sounds or loud sounds.

At operation226, a classification model can be applied using the transformed media data as an input to the convolutional neural network. The convolutional neural network can analyze various features of the transformed media data201(e.g., over a time window of several seconds) to attempt to identify whether some specified feature or event is present or not. Such a convolutional neural network can include thousands to millions of parameters that can be tuned (e.g., weighted) for specific media events (e.g., whistling noises, dog barks, presence of music, etc.) or can be tuned for specific situations (e.g., media acquired by way of a mobile device, subject to environmental noise, etc.). Different classification models can be associated with unique combinations of tuned parameters.

In an example embodiment, applying a classification model at operation226includes detecting various events or characteristics in a query. For example, the media processor circuit110can be configured to populate, for a given query, a profile across ‘n’ categories including assigning a value for each feature or category based on similarities between the extracted features of the media data201and the features of ‘n’ classification models. In an example embodiment, applying a classification model at operation226includes detecting whether a query includes (or is likely to include) a specified event, such as an auditory event (e.g. whistle, dog-bark, presence of music, etc.). The classifier can optionally be used to classify discrete audio samples, or it can be used substantially continuously to classify a stream of received audio, for example, by processing a series of samples in sequence or at a specified interval.

In an example embodiment, a context parameter203can be used to trigger a classification process. Additionally or alternatively, a classification model can be selected for use based on the context parameter203. In an example embodiment, the context parameter203can be derived from the media data201itself, such as by identifying changes in frequency content, amplitude, or in some other characteristic of the media data201signal. In an example embodiment, the context parameter203can be based on a signal from another source or sensor that is associated with the media data201. For example, the context parameter203can be based on a signal from an accelerometer, a user input, a location sensor, a clock or timer circuit, or other context-providing indication that corresponds with the media data201.

In an example embodiment, the media processor circuit110(or other processor circuit, such as associated with an input device to the network-based media classifier105) can be configured to monitor the media data201for a specified event. The media data201can represent a media stream, such as an audio stream that is continuously or intermittently sampled. In this example, the media processor circuit110can be configured to operate in an “always on” mode where media classifications can be performed, e.g., at operation206, at various intervals, or can be performed in response to some triggering event, such as a triggering event that exceeds a specified threshold change in the media data201or a specified threshold change in the context parameter203.

In an example embodiment, the media processor circuit110(or other processor circuit, such as associated with an input device to the network-based media classifier105) can be configured to scan a fixed audio segment or sample to identify one or more sample characteristics, such as to determine whether the sample includes music, speech, or some other media type. If a specified event is detected, then a media classification request can be initiated at operation206. For example, the media processor circuit110can be configured to monitor an audio channel of a baby monitor. When the classifier identifies a change in the frequency content of a signal in the audio channel, the signal can be classified at operation206to determine whether it includes, or is likely to include, a baby's cry (e.g., as distinguished from other noises like talking or rustling). If a baby's cry is positively indicated, e.g., at operation210, then a caregiver can be notified.

At operation206, classifying the media data201can include providing a media type probability index for the media data201. The media type probability index includes an indication or likelihood that the media data201corresponds to a specified media type. For example, the network-based media classifier105can be configured to receive the media data201or query and then transform the received query into a specified frequency or spectral space. The media processor circuit110can then apply a convolutional neural network to generate an indication of a probability or likelihood that the query corresponds to a particular media type. In an example embodiment, the result includes an indication of a relative likelihood that an audio sample corresponds to speech, music, or some combination thereof. Based on other results from the neural network, the results can indicate a likelihood that the audio sample corresponds to some specified audio characteristic or classification, such as a specified song. An output of the neural network can include an audio event probability, or an indication of how likely it is that a particular query corresponds to a specified or designated audio type.

In an example embodiment, at operation206, classifying media includes using a classification model to narrow a universe of available or potential classifications for a particular sample or user. For example, a classification model can include various weights or other means to constrain available paths in a convolutional neural network. In an example embodiment, the first and second users132or152and/or their respective devices130or150, can be associated with different classification models, and the models can be applied by the media processor circuit110to more efficiently classify media. In an example embodiment, a classification model can be selected based on some a priori knowledge of a source device or user history. For example, a particular classification model, from among multiple different available classification models, can be selected for use when the query includes an audio signal sampled from a mobile telephone's microphone.

In an example embodiment, a first query includes media data201that includes classical music, and the first query corresponds to a first parameter that indicates that the query includes classical music. The first query can be processed using a neural network configured by a first classification model, and the first classification model can “tune” the network to more efficiently identify classical music due to various weights, filters, or other components of the network that are selected or structured to discern among different characteristics of classical music. A second classification model, such as tuned for recognizing pop music, may not successfully or efficiently identify the first query. Similarly, a second query that includes spoken word content may not be successfully or efficiently classified using either of the first classification model tuned for classical music or the second classification model tuned for pop music.

Various classification models can be applied to provide media classification at operation210, such as by loading a model stored locally on a device, or by receiving a model via the network-based media classifier105. Multiple models can optionally be used or applied in series. In an example embodiment, a model can be particular to a specified class of input devices (e.g., devices used to receive or provide the query or media data201). For example, a first model can be configured to classify speech and music when received from a mobile telephone microphone. A second model can be configured to classify speech and music when received from a mobile telephone's native sound board. A third model can be configured to identify whether query data includes one or both of speech and music. A fourth model can be configured to classify audio from a television broadcast source. Depending on a detected situation or context, such as based on one or more context parameters, one or more classification models can be selected for use in the method200. In some example embodiments, a model is chosen automatically by detecting the source device, or by comparing the quality of competing models, or by some analysis of the query to be analyzed, among other criteria.

FIG. 3illustrates generally a block diagram of some components of a mobile device. In an example embodiment,FIG. 3shows a block diagram corresponding to various components of the first mobile device130. The second mobile device150can optionally include the same or similar components. In the example embodiment ofFIG. 3, the first mobile device130includes a first processor circuit310, a transceiver circuit320, an accelerometer330, and an audio playback circuit340. The example embodiment includes a microphone350, a location sensor360, a signal feature generator370, and a class archive380. The various components of the first mobile device130can be configured to communicate with each other (e.g., via a bus, shared memory, or a switch).

The transceiver circuit320can be configured to enable communication between the first mobile device130and the network190, for example, using a wired or wireless communication protocol. In an example embodiment, the transceiver circuit320is configured for WiFi or cellular network communications with the network190. In an example embodiment, the transceiver circuit320is configured to receive a broadcast signal, such as a television signal, a radio signal, or other signal that includes digital or analog media signal information.

In an example embodiment, the audio playback circuit340, microphone350, location sensor360, signal feature generator370, and class archive380can form all or part of an app390, such as can include a software application configured to be executed or instantiated on a mobile hardware device or one or more other devices. In an example, the app390is stored (e.g., installed) on the first mobile device130and is executable by the first mobile device130by one or more processor circuits, such as the first processor circuit310. In an example embodiment, the app390is stored, at least in part, at the cloud118, and the app390interfaces with one or more of the components of the first mobile device130. In an example embodiment, the one or more processor circuits in the first mobile device130can be included (e.g., temporarily or permanently) in the app390, transceiver circuit320, accelerometer330, or any suitable combination thereof. In an example embodiment, the first mobile device130is configured to interact with the network-based media classifier105via the app390to classify media present at (e.g., media played by) or near (e.g., media able to be received or sensed by) the first mobile device130.

In the example embodiment ofFIG. 3, the first mobile device130includes various inputs that are configured to sample or receive media information. For example, audio signal information can be sampled from the audio playback circuit340, or audio signal information can be received using the microphone350. In an example embodiment, the first mobile device130further includes one or more sensors or inputs that can be used to identify or provide corresponding one or more context parameters. For example, the location sensor360can include a GPS sensor, a WiFi transceiver, or other sensor that can be configured to determine or provide information about a location of the first mobile device130. In an example embodiment, the first mobile device130is associated with one or more context parameters by programming or by a user-input.

The signal feature generator370can be included at the first mobile device130, at the network-based media classifier105, or at some other location or device that is communicatively coupled with the first mobile device130or the network-based media classifier105. The signal feature generator370can be configured to identify or extract various media data features, such as spectral, temporal, or other features, from a media signal provided by the first mobile device130. For example, the signal feature generator370can receive an audio sample from the audio playback circuit340or from the microphone350and then extract, using digital signal processing (DSP) methods, various features from the audio sample. In an example embodiment, mel-frequency cepstral coefficients (MFCC), spectral flatness, mean value, zero crossings, spectral centroid, chord class, or percussiveness, among other features, can be determined or provided by the signal feature generator370based on a media sample. The various features extracted or identified using the signal feature generator370can be considered to be a fingerprint that uniquely corresponds to the signal to be identified or classified.

The class archive380can include one or more indications of classifications, classification models, or other information that can be applied by the app390(e.g., in coordination with other components of the network-based media classifier105) to classify query media. The classifications, classification models, or other information can be associated with the first user132and/or with the first mobile device130, and can be applied to narrow a search in a convolution neural network to identify or classify a media sample. In an example embodiment, the class archive380includes a user identification code that is applied by the network-based media classifier105to retrieve, from the database115, one or more classification models associated with the first user132and/or with the first mobile device130. In an example embodiment, the class archive380includes various classification models that can be applied by the app390and/or by the network-based media classifier105to classify media provided by the first mobile device130.

FIG. 4illustrates generally an example that includes a query sound waveform and a context sensor signal. The query sound waveform can comprise all or a portion of media data201that is indicated to be identified or classified. In an example embodiment, the query sound waveform410can be received using the audio playback circuit340or the microphone350of the first mobile device130.

FIG. 4includes the context sensor signal420, such as can be received from the first mobile device130, or can be determined using information about the first mobile device130and/or a user. For example, the context sensor signal420can include information from the location sensor360that indicates a location of the first mobile device130. In an example embodiment, an audio sample412can include a segment of the query sound waveform410. The audio sample412can be used as the media data201for classification, for example, according to the method200.

The context sensor signal420can represent context information that corresponds to the query sound waveform410. For example, the context sensor signal420can be received concurrently with the query sound waveform410. In the example embodiment ofFIG. 4, the context sensor signal420includes position information, such as can be determined using the accelerometer330of the first mobile device130. The context sensor signal420includes, for example, a binary indication of whether the first mobile device130is at rest (signal low) or in motion (signal high). Context information can be sampled from the context sensor signal420at various intervals, or context information can be sampled when a change is identified in the context sensor signal420. For example, first context data421can correspond to a first state change in the context sensor signal420. Second and third context data422and423can correspond to periodic samples of the context sensor signal420, for example, after some threshold context event is identified. Other context information can additionally or alternatively be used, as further discussed herein.

FIG. 5illustrates generally a block diagram of a media processor circuit, such as the media processor circuit110. The media processor circuit110can include various components that can work together with, or independently from, the components of the first mobile device130(see, e.g.,FIG. 3). In the example embodiment ofFIG. 5, the media processor circuit110includes a query receiver510, a classification model selector520, and a media type probability index generator530. In an example embodiment, the media processor circuit110further includes a signal feature generator570.

In an example embodiment, the query receiver510, the classification model selector520, and the media type probability index generator530can form all or part of an app590that is stored (e.g., installed) at the network-based media classifier105, and is executable by the media processor circuit110, or by one or more other processor circuits, to perform media classification services. In an example embodiment, the app590is stored, at least in part, at the cloud118, and the app590interfaces with one or more of the components of the first and second mobile devices130and150.

The query receiver510can be configured to receive query media, such as the media data201, that is indicated for classification. In an example embodiment, the query receiver510receives the media data201from one of the first and second mobile devices130and150. The query receiver510optionally acts as a gate to the other components of the media processor circuit110, such as by verifying that the media data201includes a query format or query signal content quality that can be processed by the media processor circuit110. In an example embodiment, the query receiver510is configured to receive one or more context parameters associated with the media data201.

The media processor circuit110can include the signal feature generator570. The signal feature generator570can be independent of, or configured for operation in coordination with, the signal feature generator370of the first mobile device130. The signal feature generator570can include code or a processing module that is configured to generate a spectrogram or other spectral content information corresponding to an audio signal sample or video signal sample (e.g., a query media sample). The signal feature generator570can be configured to identify or extract various features from the media data201received by way of the query receiver510. For example, the signal feature generator570can receive an audio signal sample from the query receiver510and then extract, using digital signal processing (DSP) methods, various features from the audio sample. Mel-frequency cepstral coefficients (MFCC), spectral flatness, mean value, zero crossings, spectral centroid, chord class, or percussiveness, among other features, can be determined by the signal feature generator370.

The classification model selector520can include software code or a processing module or circuit that is configured to use information about at least one context parameter (e.g., a context parameter received using the query receiver510) to select a classification model for use in classifying a media sample. The at least one context parameter can optionally be derived by the classification model selector520or by another module, such as by analyzing the query media to be identified, for example, to determine whether the query media includes music, speech, or a combination of music and speech.

In an example embodiment, the classification model selector520communicates with the database115to retrieve one or more classification models from the database115. An example of the database115is discussed below atFIG. 6. The classification model selector520and/or the database115can provide recommendations for various classification models to use, for example, based on the context parameter(s) and/or spectral features (e.g., identified by the signal feature generator370or570) associated with the query to be identified.

The media type probability index generator530is configured to apply the spectral feature information from the signal feature generators370and/or570to a classifier network to generate a probability index, or likelihood, that a query corresponds to a specified media type. In an example embodiment, a media type can include a media category (e.g., spoken word, music, classical music, newscast, etc.) or a media type can include an indication of a specified source or specified work, such as a song, TV program (e.g., including an indication of a specific episode), or performer. That is, in an example embodiment, the media type probability index generator530can provide an indication of a likelihood that a query corresponds to a specified recording of a specified song by a specified artist. In another example embodiment, the media type probability index generator530can provide an indication of a likelihood that a query corresponds to a specified artist.

FIG. 6illustrates generally a block diagram of a database for storing classification models. The block diagram ofFIG. 6can correspond to the database115. The database115can be communicatively coupled with the media processor circuit110and/or the network190to provide, among other things, various classification models and neural network data621for classifying media. The neural network data621can include various structural, filter, or weighting information for use in a convolutional neural network, or in some other similar tool that can provide model-based classification services.

In an example embodiment, the database115inFIG. 6includes a classifier610that includes multiple classification models601,602, and603. A first classification model601is a function of multiple parameters (PARAM[1], PARAM[2], . . . , PARAM[n]). That is, given a set of parameters, such as including but not limited to context parameters, the classifier610can return a corresponding classification model for use by the media processor circuit110. The parameters can correspond to context parameters, spectral features, or other information associated with one or more of a query, device, user, or other input. A second classification model602can be a function of the same or different multiple parameters. The classifier610can include n classification models that correspond to different combinations of parameters. In an example embodiment, a specified combination of parameters can return multiple different classification models. In an example embodiment, a specified combination of parameters can indicate that several different classification models are selected and can be serially applied.

In an example embodiment, the database115can be queried by the media processor circuit110whenever at least one parameter changes. For example, in response to a change in a context parameter received from the first mobile device130, the media processor circuit110can query the database115to determine whether an updated classification model is indicated by the changed context parameter. If an updated classification model is not indicated, then the media processor circuit110can perform its media classification service using a previously received model. If an updated classification model is indicated, then the classifier610can transmit information about the new classification model to the media processor circuit110for use in present or subsequent classification operations.

FIG. 7illustrates generally an example of using a classification network to identify media content.FIG. 7includes an example700of using a classification network, such as a convolutional neural network tuned for a particular media type, to classify media. In the example embodiment, the first user132and the first mobile device130are illustrated as traveling between a first location701(e.g., a music venue) and a second location702(e.g., the street outside of the music venue). In the example embodiment ofFIG. 7, the first mobile device130can be configured to sample first audio data711. In an example embodiment, the first audio data711is representative of live music being played at the first location701.

The first mobile device130can be further configured to identify first context data712. In an example embodiment, the first context data712includes one or more of GPS location information identifying the first location701, user information about the first user132(e.g., calendar entry information, owned music library information, playlist or listening history, etc.), or other information that can be used to identify one or more of the first location701and the first user132.

In an example embodiment, a media identification query can be generated by the first mobile device130to identify audio content at the first location701. The media identification query can be generated automatically by the first mobile device130, such as based on a specified schedule or on a detected change in ambient sound content of the first location701, or the query can be generated in response to a user input (e.g., received from the first user132) at the first mobile device130.

The first mobile device130, such as using the app390, can be configured to provide the first audio data711to the network-based media classifier105. In an example embodiment, the first mobile device130additionally provides the first context data712to the network-based media classifier105. At the network-based media classifier105, a first classification model715can be retrieved from the database115, and then applied using the media processor circuit110to provide a media type probability index that corresponds to the first audio data711. The first context data712can influence which one or more of n available classification models is selected for use with the first audio data711. In an example embodiment, the media processor circuit110applies the first classification model715in a convolutional neural network to identify, in the first audio data711, a first indication of media content718, such as corresponding to a song being played at the first location701. In an example embodiment, applying the first classification model715includes applying various weights or other parameters in a convolutional neural network, such as using the media processor circuit110, to process and identify the first audio data711.

In the example embodiment ofFIG. 7, the first user132transitions or relocates from the first location701to the second location702. In response to the detected change in location, such as indicated by a change from the first context data712to the second context data722, the first mobile device130can sample second audio data721that is representative of the new environment, that is, representative of the second location702. The sampling and re-sampling of the audio data can be triggered in various ways. In an example embodiment, the sampling can be performed automatically in response to a detected change in the location or context of the first user132. In an example embodiment, the sampling can be performed in response to a user input at the first mobile device130. In an example embodiment, the sampling can be performed periodically or at some specified interval. The sampling can be triggered in response to other detected changes associated with the first user132and/or with the first mobile device130.

The second audio data721, such as together with the second context data722, can be provided to the network-based media classifier105. The network-based media classifier105can optionally identify a different classification model, such as a second classification model725, to apply using the convolutional neural network to generate an indication of a media type for the second audio data721. In an example embodiment, the media processor circuit110applies the second classification model725at the classification network730to identify, in the second audio data721, a second indication of media content728, such as corresponding to a different second song being played at the second location702. In the example embodiment ofFIG. 7, the different second song could be played by an outdoor speaker system of a nearby restaurant.

FIGS. 8-11illustrate generally examples of determining media type probabilities, selecting different classification models for use in a classification network (e.g., a convolutional neural network trained to identify various media), and using context parameters, among other things.FIGS. 12 and 13illustrate generally examples of training a neural network, and determining when or whether to expend computational resources to provide media classification services, such as using a convolutional neural network, based on one or more characteristics of a media query to be identified. Each of the methods can be implemented using code that can be executed using the systems, methods, or devices described herein.

FIG. 8illustrates generally an example embodiment that includes determining an audio type probability index. The example embodiment can include a first process800comprising multiple different steps or operations. At operation810, the example embodiment includes accessing audio data or an audio signal that represents a query sound to be identified. Accessing audio data can include, among other things, receiving or identifying an audio sample using various portable or fixed electronic devices, such as including cellular telephones, televisions, or tablet computers, among other devices. In an example embodiment, accessing audio data includes monitoring the audio playback circuit340or sampling ambient audio information using the microphone350of the first mobile device130.

At operation820, the example embodiment includes accessing a first context parameter. The first context parameter can correspond to the query sound from810, and/or the context parameter can correspond to a device, a user, or can otherwise be associated with the query sound accessed at operation810. A context parameter that corresponds to a query sound can include time-varying context signal information that is received substantially concurrently with, or recently before or after, the query sound. In this manner, the context parameter can provide information about what is or what may be occurring at or near the location or time at which the query sound is accessed. In an example embodiment, a context parameter that corresponds to a query sound can be substantially time-invariant, such as can include information about a user identification, or a device type, among other things.

In an example embodiment, accessing the first context parameter at operation820includes receiving location information from the location sensor360of the first mobile device130. For example, the first context parameter can include location information (e.g., GPS or other coordinates) that is determined using the location sensor360. The location information can be associated with a particular query sound that is recorded or sampled when the location was determined.

At operation830, the example embodiment includes determining spectral features of audio data, for example, of the audio data accessed at operation810. Determining spectral features can include, among other things, using digital signal processing (DSP) methods to identify mel-frequency cepstral coefficients (MFCC), spectral flatness, mean value, zero crossings, spectral centroid, or other extractable features. Various digital signal processing feature extraction methods and standards (e.g. MPEG-7) can be used. Determining spectral features at operation830can include using one or more of the first processor circuit310of the first mobile device130, the media processor circuit110of the network-based media classifier105, or some other processor or circuit to receive and analyze a digital audio sample that is to be classified.

At operation835, the example embodiment can include determining a likelihood that the query sound can be positively classified based on a query sound characteristic or based on a context parameter characteristic. The query sound and/or context parameter can optionally be analyzed (e.g., using one or more of the first processor circuit310of the first mobile device130, the media processor circuit110of the network-based media classifier105, or some other processor or circuit to receive and analyze a digital audio sample that is to be classified) to identify a characteristic or feature that can be used or compared to a specified threshold characteristic or threshold feature to provide an indication of a likelihood that the query can be classified. In an example embodiment, operation835can include using the spectral feature(s), determined at operation830, to determine the likelihood that the query sound can be positively identified.

Although operation835is illustrated as following operations820and830, the determination at operation835can be performed substantially immediately following acquisition of the query sound (e.g., following operation810), or can be performed for the context parameter substantially immediately following acquisition of the context parameter (e.g., following operation820). Based on the result of operation835, the process800can continue at operation840. For example, if the likelihood determined at operation835is above a specified threshold likelihood that the query sound can be identified, then the process800can continue at operation840with selecting a classification model for use in classifying the query sound. If the likelihood determined at operation835is below a specified threshold likelihood, then the process800can terminate, or can re-sample one or more of the query sound and/or the context parameter, such as to attempt to acquire a better quality signal for use in a media classification process. An example embodiment that includes determining whether to proceed with a classification, such as based on a characteristic of a media query, is included herein in the discussion ofFIG. 13.

At operation840, the example embodiment includes selecting a classification model for use with the audio data, for example, to be used to identify or classify the audio data. In an example embodiment, selecting the classification model at operation840includes using at least one context parameter. The at least one context parameter can be one of multiple parameters used to select or identify a classification model (see, e.g., classification models601-603in the example embodiment ofFIG. 6). In an example embodiment, the media processor circuit110is configured to retrieve a classification model from the database115in the network-based media classifier105. The retrieved classification model can include parameters for use with a neural network that can be tuned (e.g., by the retrieved model) to identify or classify the query sound based on the audio data.

At operation850, the example embodiment includes determining an audio type probability index for the query sound using the first classification model. In an example embodiment, determining the audio type probability index includes performing comparative analyses of the various spectral features (e.g., determined at operation830) at one or multiple nodes in a neural network to identify an audio type or a probability index that indicates that the audio data is likely to correspond to a specified audio type or audio content. In an example embodiment, determining the audio type probability index includes using the audio data, the first classification model, and a trained classifier network. In an example embodiment, determining the audio type probability index includes classifying or identifying the query sound by title, artist, source, or otherwise positively identifying a correlation between the query sound and a particular characteristic.

At operation860, the example embodiment can include storing information about whether the audio type probability index, such as determined at operation850, indicates that the query sound was successfully or unsuccessfully classified, that is, whether the query was properly identified, misidentified, or not identified. Operation860can optionally be performed during normal system use, or during a training process. An example of a training process is illustrated below atFIG. 12.

At operation860, whether a classification operation is successful or not can be determined automatically, such as by identifying that one or more characteristics of the query sound, the context parameter, and/or the audio type probability index, exceeds a specified threshold characteristic (i.e., by using an objective measure). In an example embodiment, whether a classification operation is successful or not can be indicated by a user responding to an inquiry about whether the user accepts or rejects a result of the classifier.

A query sound can be unsuccessfully classified or misidentified for numerous reasons. For example, a poor quality or noisy media signal can be difficult to analyze, or can yield spectral features or a signal fingerprint that does not accurately correspond to underlying media signal content, such as due to masking by noise or other signal anomalies. Noise can be introduced in a media signal when the media signal is sensed using an environment sensor like a microphone, or can be introduced by electrical or electromagnetic noise, or via other means. A poor quality or noisy context parameter can be similarly difficult to analyze if it is noisy or if it includes anomalies that lead to a misinterpretation. If one or more of the query sound and/or the context parameter are noisy or inaccurate, then the classification model selected at operation840can be improper and, as a result, the determined audio type probability index can be poor.

In an example embodiment, the information stored at operation860includes relative or absolute information about the success (or lack of success) in classifying or identifying the query sound, and optionally includes information about a correspondence between the success and one or more characteristics of the query sound, the context parameter, or the classification model(s) used. The stored information can be later retrieved and compared to a new query sound, context parameter, or model, such as for use in identifying a likelihood that a classification of the new query sound will be successful. If the likelihood of success is too low, then a classification process can be suspended in order to preserve computational resources or power for other tasks.

FIG. 9illustrates generally an example embodiment900that includes selecting a classification model based on context parameters. The example embodiment can include selecting among multiple different classification models that are available for use in a classification network.

At operation910, the example embodiment includes accessing first and second context parameters corresponding to a query sound. The first and second context parameters can be of the same type or format (e.g., both can include location information or coordinates, time stamp information, user profile information, etc.), or the context parameters can be different. One or both of the first and second context parameters can be determined using, or can be derived from, an audio sample or query sound to be identified. One or both of the context parameters can be determined based on sensor information, user information, or other information that can indicate a characteristic of an audio sample, or can indicate a characteristic of a user or device that is associated with an audio sample. In an example embodiment, the media processor circuit110and/or the first processor circuit310can access the first and second context parameters at operation910.

At operation920, the example embodiment includes determining a search scope characteristic that is associated with each of the first and second context parameters. For example, a first search scope can be associated with the first context parameter, and a different second search scope can be associated with the second context parameter. In an example embodiment, the first context parameter includes GPS coordinates for the first mobile device130. A first search scope associated with the GPS coordinates can be relatively narrow, for example, if it can be determined that the GPS coordinates correspond to the first location701. In this case, a search scope characteristic can optionally be narrowed to include only media context that is known to be associated with the first location701(e.g., as can be determined by a calendar of events associated with the first location701, or other prior or programmed information about the first location701).

In an example embodiment, the second context parameter includes an indication of a device type that was used to acquire query data. In this example, the second context parameter can include information indicating that audio data or a query sound of interest was acquired using the first mobile device130. Using information about the source of the query sound, for example, a search scope characteristic can be narrowed or tuned to identify media that is most likely to be received by a mobile device. Additionally or alternatively, one or more pre-processing filters can be tuned to optimize spectral information sampled from a mobile device.

At operation930, the example embodiment includes selecting one of the first and second context parameters. The selected one of the context parameters can be used to select a classification model at operation940(see, e.g.,FIG. 8at operation840). That is, the selection of a particular classification model at operation940can be based on, or informed at least in part by, the selected one of the context parameters.

In an example embodiment, a context parameter that is selected for use can be a context parameter that corresponds to a relatively narrower search scope. When the search scope is narrower, performing media classification can be more focused and less processing resources are thus consumed. Accordingly, in the example embodiment above, the first context parameter can be selected when the GPS coordinates correspond to a smaller or more narrow set of potential media classifications, such as compared to the second context parameter that can correspond to any or all media that can or is likely to be sampled by a mobile device.

Selecting the classification model at operation940can include using the selected one of the first and second context parameters. In some examples, different weights can be associated with different context parameters, and multiple context parameters can be used to select a classification model (see, e.g.,FIG. 6at classifier610that includes multiple classification models, and each different model can be a function of multiple different parameters). Selecting the classification model at operation940can include using the media processor circuit110to retrieve the selected classification model and to apply the selected classification model in the context of a neural network that is configured to provide an indication of a media type.

FIG. 10illustrates generally an example embodiment1000that includes selecting an updated classification model. The example embodiment can include receiving and parsing updated context parameter information. Some portions ofFIG. 10correspond generally to the example embodiment illustrated atFIG. 7.

At operation1010, the example embodiment can include identifying a classification model update trigger. Identifying an update trigger can include, among other things, determining that a query sound characteristic has changed (e.g., determining that one or more of a tone, amplitude, timbre, percussiveness, or other quality is different than in an earlier query sound or sample), determining that an update interval has elapsed, or determining that a context parameter associated with a query sound has changed by more than a specified threshold amount. Referring again to the example atFIG. 4, an update trigger can be provided, for example, based on the first context data421, such as can be associated with a change in the state of the context sensor signal420. In an example embodiment, the classification model update trigger can be identified by a source device (e.g., by the first or second mobile device130or150), by the media processor circuit110, by a user input to a source device and/or to the network-based media classifier105, or by other means.

At operation1020ofFIG. 10, the example embodiment includes receiving at least one updated context parameter. For example, in response to identifying the classification model update trigger, the media processor circuit110or other component can request an updated context parameter, such as to evaluate whether a classification model update is required or desired. At operation1020, the example embodiment can include receiving an updated context parameter based on a change in a continuous or intermittent query stream, or receiving the updated context parameter based on a detected change in device status (e.g., a change in location or change in user). In the context ofFIG. 7, for example, receiving an updated context parameter can include using the first mobile device130(or other device or process) to receive and recognize the first context data712and the different second context data722.

If more than one context parameter update is identified, then at operation1030the example embodiment includes selecting at least one of the updated context parameters for use in selecting a classification model. In an example embodiment, a context parameter corresponding to a narrower classification of media is selected at operation1030, such as to narrow the scope of a search performed by the network-based media classifier105. At operation1040, the example embodiment includes selecting a classification model based on the updated context parameter(s). The selected classification model can be applied as described above for classifying query media associated with the updated context parameter(s).

FIG. 11illustrates generally an example embodiment1100that includes determining when to apply an updated classification model. For example, various considerations can be evaluated to determine whether or when to expend resources in updating a context parameter, or retrieving or applying an updated classification model.

At operation1110, a processor circuit such as the media processor circuit110can determine whether a specified duration has elapsed since a prior query was classified. The specified duration can represent a minimum “retry” or “reclassify” interval between queries. At operation1105, if the specified duration has not elapsed, then the system can pause for a specified update interval. Upon expiration of the update interval, the query can proceed.

At operation1120, the media processor circuit110, the first processor circuit310, or other processor can be used to analyze a query for quality. If the query includes audio signal information that is deemed to be too noisy or too quiet (i.e., the query has insufficient signal content) for use in a classification network, then the method can return to1105. After pausing for a specified interval, a query can be re-sampled such as to see if noise or amplitude levels have changed. In some examples, some networks or classification models can be more tolerant of noise. In other examples, a priority indication or user-override can be provided to force the network to process a noisy signal.

If the query is determined to have a sufficient minimum signal quality, then at operation1130the example embodiment can include determining whether the query itself has changed by more than some specified threshold amount. If the query itself is reasonably unchanged from a prior query (e.g., in frequency content, tone, percussiveness, or other quantitative or qualitative measure), then the system can assume that a prior query still represents present query content to be identified, and a reclassification can be avoided or delayed. The example embodiment can include a return to1105to pause, and a new query can be generated after a specified interval or after identification of a trigger event.

If the query is sufficiently different from an earlier query, then at operation1140, the example embodiment can include determining whether a context parameter associated with the query has changed by more than a specified threshold amount. If the context parameter has not changed, or if a new context parameter is not available, then the example embodiment can continue at operation1145by selecting or applying a prior classification model to the query. If the context parameter is determined to have changed by more than the specified threshold amount, or if a new context parameter is available, then the example embodiment can continue at operation1150by determining whether the changed or new context parameter itself meets some specified minimum signal quality. For example, if a context parameter includes noise, or represents an unknown context, then a prior classification model can be used (e.g., at operation1145). If, however, the context parameter meets the quality threshold, then the example embodiment can continue at operation1160by selecting and applying a new classification model, based on the new context parameter, to classify the query.

FIG. 12illustrates generally an example embodiment1200that includes training a media query identification system. The example embodiment can include a process that identifies a tolerance for changes in one or more input signal characteristics. For example, the process can be used to determine how much noise can be tolerated in a media query signal. An amount of noise or variation from a pure or expected media query and/or context parameter can be referred to as a tolerance. If too much noise is present in an input signal, then the noise level exceeds a specified tolerance threshold, and it can be unlikely that a media type classification based on the noisy input signal will be accurate. If a noisy signal can be identified early, such as before an attempt is made to identify or classify a query, then a system can preserve computing resources and/or power by inhibiting media classification services. If less than a specified threshold amount of noise (or other signal characteristic anomaly) is present in a media query, then a system can carry out the media classification process with a reasonable likelihood of success. That is, if an input signal has a noise (or other signal anomaly characteristic) that is less than a specified noise tolerance threshold, then the system can proceed with media classification of the input signal.

At operation1210, the example embodiment includes accessing a media query for training. At operation1220, the example embodiment includes accessing a context parameter for training. In an example embodiment, the accessed media query and/or context parameter can be pre-selected, such as by an individual or machine tasked with training a media classification system. At operation1212, a media training parameter can be introduced. Introducing the media training parameter can include, among other things, adding or changing a noise characteristic, a frequency profile, a frequency filter, or other characteristic of the original media query. That is, introducing the media training parameter at operation1212can include intentionally modifying the content or one or more characteristics of the original media query to provide an updated media query at operation1214. In an example embodiment, the media training parameter corresponds to a specified tolerance threshold, or the media training parameter can be selected to exceed a prior tolerance threshold, such as to test or train one or more new conditions with the classification system.

At operation1222, a context training parameter can be similarly introduced for the context parameter accessed at operation1220. Introducing the context training parameter can include intentionally modifying the content or one or more characteristics of the original context parameter to provide an updated context parameter at operation1224. In some example embodiments, at least one of the media training parameter and the context training parameter is not used, and the updated media query provided at operation1214is the same as the original media query accessed at operation1210, or the updated context parameter provided at1224is the same as the original context parameter accessed at operation1220.

At operation1230, a first classification model can be selected. The first classification model can be selected based on one or more of the media query, the updated media query, the context parameter, or the updated context parameter. In an example embodiment, the classification model can be selected by a user tasked with training a system, or can be selected automatically by a machine. At operation1240, the example embodiment can use the first classification model to attempt to classify the updated media query. Operation1240can include one or more of providing an indication of a media type (e.g., including an indication of a media source, origin, or other identifying characteristic) corresponding to the media query, or the operation can include providing a likelihood that the media query corresponds to a specified media type.

At operation1250, the example embodiment can include determining whether the media query was successfully identified, such as based on a priori information about the original media query that was accessed at1210. If the media query is successfully identified, then the example embodiment can continue at operation1260with updating a tolerance threshold. For example, at operation1260, a tolerance threshold, such as for use in later classification tasks for other media queries, can be updated to correspond to the media training parameter introduced at operation1212. In an example embodiment, the media training parameter introduced at operation1212includes a first noise profile and, when a media query having the first noise profile is successfully classified at operation1250, then a tolerance threshold corresponding to the first noise profile can be selected for use in later classification tasks for the same or other media queries.

At operation1250, if the media query is not successfully identified, then the example embodiment can continue at operation1270with updating one or both of the media training parameter and the context training parameter. In an example embodiment, updating the training parameters at operation1270includes selecting for further analysis a training parameter that corresponds to a different tolerance threshold than was previously used. In an example embodiment, at operation1270, a tolerance threshold, such as for use in later classification tasks for other media queries, can be updated to correspond to a media training parameter that is previously known to yield successful classifications. In an example embodiment, the media training parameter introduced at operation1212includes a first noise profile and, when a media query having the first noise profile is unsuccessfully classified at operation1250, then a tolerance threshold corresponding to a different second noise profile can be selected for use in later classification tasks for the same or other media queries.

FIG. 13illustrates generally an example embodiment1300that includes determining whether to classify a media query. In an example embodiment, a media query can be previously acquired or accessed, or a media query can be received periodically or intermittently. At operation1310, the example can include pausing for a specified minimum duration or update interval, such as a specified time from a previous classification event. In an example, after the update interval is elapsed, or in response to a user request for media classification, a noise characteristic of a media query can be identified at operation1320. In an example embodiment, the noise characteristic can be identified using the media processor circuit110, or using some other processor circuit, such as can be associated with an input device to the network-based media classifier105. For example, the first processor circuit310of the first mobile device130can be used to identify a noise characteristic of a media query received by or prepared by the first mobile device130. Based on the noise characteristic identified at operation1320, a media classification system can be caused to initiate or inhibit a media classification process. It can be advantageous to inhibit or prevent a media classification process from proceeding, for example, to conserve processing capacity of one or more processor circuits for other tasks.

At operation1330, the example embodiment includes comparing the noise characteristic identified at operation1320with a specified noise tolerance threshold. The noise tolerance threshold can be specified by a user, by a programmer, or the noise tolerance threshold can be learned by a system employing various network training operations (see, e.g., the example embodiment ofFIG. 12). At operation1332, if the identified noise characteristic is determined (e.g., by the media processor circuit110, by the first processor circuit310, or by another processor circuit associated with a device in the network environment100) to exceed a specified noise tolerance threshold, then the query can be deemed to be too noisy to classify and the example can return to operation1310to pause for an update interval. After the update interval elapses, the example could continue, for example, by sampling or acquiring a subsequent query with a different noise characteristic, and the example could include analyzing the subsequent query to determine whether its different noise characteristic is more suitable for classification. If, at operation1332, the identified noise characteristic does not exceed the specified noise tolerance threshold, then the example can continue at operation1350with classifying the query. For example, the embodiment can continue at operation840in the example ofFIG. 8by selecting a classification model for use with the query.

At operation1340, the example embodiment includes comparing the noise characteristic identified at operation1320with a result of a prior classification attempt where the prior classification attempt is for a query having the same or similar noise characteristic. If a prior classification attempt was not successful at identifying or classifying the query having the same or similar noise characteristic, then query classification can be avoided and the example embodiment can return to operation1310. If, at operation1342, a prior classification attempt was successful at identifying or classifying the query having the same or similar noise characteristic, then a query classification process can commence or continue, such as at operation840in the example ofFIG. 8.

Although the example embodiment ofFIG. 13refers to a noise characteristic of a query, other characteristics can be similarly analyzed. For example, instead of a noise characteristic, a frequency content characteristic can be identified and used to determine whether to classify a query. In an example embodiment, a visual characteristic such as a color content characteristic can be identified and used to determine whether to classify a query that includes visual media or video information. Other media characteristics can be similarly analyzed and used as a gate to initiating or inhibiting media classification processes.

FIG. 14is a block diagram illustrating components of a machine, according to some examples, that can read instructions from a machine-readable medium and perform any one or more of the methods discussed herein. In the example embodiment ofFIG. 14, a machine1400, according to some example embodiments, is able to read instructions1424from a machine-readable medium1422(e.g., a non-transitory machine-readable medium, a machine-readable storage medium, a computer-readable storage medium, or any suitable combination thereof) and to perform any one or more of the methods discussed herein, in whole or in part.FIG. 14shows the machine1400in the example form of a computer system (e.g., a computer) within which the instructions1424(e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine1400to perform any one or more of the methodologies discussed herein can be executed, in whole or in part.

In an example embodiment, the machine1400operates as a standalone device or can be communicatively coupled (e.g., networked) to other machines. In a networked deployment, the machine1400can operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a distributed (e.g., peer-to-peer) network environment. The machine1400can be a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a cellular telephone, a smart phone, a set-top box (STB), a personal digital assistant (PDA), a web appliance, a network router, a network switch, a network bridge, or other machine capable of executing the instructions1424, sequentially or otherwise, that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall be understood to include any collection of machines that individually or jointly execute the instructions1424to perform all or part of any one or more of the methods discussed herein. In an example embodiment, the machine1400includes a device that can be used as an input to the network-based media classifier105, or the machine1400can include all or a portion of the network100.

In the example embodiment ofFIG. 14, the machine1400includes a processor circuit1402(e.g., one or more central processing units (CPUs), one or more GPUs, one or more digital signal processors (DSPs), one or more application specific integrated circuits (ASICs), one or more radio-frequency integrated circuits (RFICs), or any suitable combination thereof), a main memory1404, and a static memory1406, which are configured to communicate with each other via a bus1408. The processor circuit1402contains solid-state digital microcircuits (e.g., electronic, optical, or both) that are configurable, temporarily or permanently, by some or all of the instructions1424such that the processor circuit1402is configurable to perform any one or more of the methodologies described herein, in whole or in part.

The machine1400can include a graphics display1410capable of displaying graphics or video, and the machine1400can include an alphanumeric input device1412(e.g., a keyboard or keypad), a pointer input device1414, a data storage1416, an audio generation device1418(e.g., a sound card, an amplifier, a speaker, a headphone jack, or any suitable combination thereof), or a network interface device1420.

In an example embodiment, the data storage1416(e.g., a data storage device) includes the machine-readable medium1422(e.g., a tangible and non-transitory machine-readable storage medium) on which are stored the instructions1424embodying any one or more of the methodologies or functions described herein. The instructions1424can also reside, completely or at least partially, within the main memory1404, within the static memory1406, within the processor circuit1402(e.g., within the processor's cache memory), or any suitable combination thereof, before or during execution thereof by the machine1400. Accordingly, the main memory1404, the static memory1406, and the processor circuit1402can be considered machine-readable media (e.g., tangible and non-transitory machine-readable media). The instructions1424can be transmitted or received over the network190via the network interface device1420. For example, the network interface device1420can communicate the instructions1424using any one or more transfer protocols (e.g., hypertext transfer protocol (HTTP)).

In an example embodiment, the machine1400is a portable computing device (e.g., a smart phone, a tablet computer, or a wearable device), and can have one or more additional input components1430(e.g., sensors or gauges), such as discussed herein. Examples of such input components1430include an image input component (e.g., one or more cameras), an audio input component (e.g., one or more microphones), a direction input component (e.g., a compass), a location input component (e.g., a global positioning system (GPS) receiver), an orientation component (e.g., a gyroscope), a motion detection component (e.g., one or more accelerometers), an altitude detection component (e.g., an altimeter), a biometric input component (e.g., a heart rate detector or a blood pressure detector), and a gas detection component (e.g., a gas sensor). Input data gathered by any one or more of these input components can be accessible and available for use by any of the components described herein.

As used herein, the term “memory” refers to a machine-readable medium able to store data temporarily or permanently and can be taken to include, but not be limited to, random-access memory (RAM), read-only memory (ROM), buffer memory, flash memory, and cache memory. While the machine-readable medium1422is shown in an example embodiment to be a single medium, the term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store instructions. The term “machine-readable medium” shall also be taken to include any medium, or combination of multiple media, that is capable of storing the instructions1424for execution by the machine1400, such that the instructions1424, when executed by one or more processors of the machine1400(e.g., processor circuit1402), cause the machine1400to perform any one or more of the methodologies described herein, in whole or in part. Accordingly, a “machine-readable medium” refers to a single storage apparatus or device, as well as cloud-based storage systems or storage networks that include multiple storage apparatus or devices. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, one or more tangible and non-transitory data repositories (e.g., data volumes) in the example embodiment form of a solid-state memory chip, an optical disc, a magnetic disc, or any suitable combination thereof. A “non-transitory” machine-readable medium, as used herein, specifically does not include propagating signals per se. In some example embodiments, the instructions1424for execution by the machine1400can be communicated by a carrier medium. Examples of such a carrier medium include a storage medium (e.g., a non-transitory machine-readable storage medium, such as a solid-state memory, being physically moved from one place to another place) and a transient medium (e.g., a propagating signal that communicates the instructions1424).

Some examples are described herein as including modules. Modules can constitute software modules (e.g., code stored or otherwise embodied in a machine-readable medium or in a transmission medium), hardware modules, or any suitable combination thereof. A “hardware module” is a tangible (e.g., non-transitory) physical component (e.g., a set of one or more processors) capable of performing certain operations and can be configured or arranged in a certain physical manner. In various example embodiments, one or more computer systems or one or more hardware modules thereof can be configured by software (e.g., an application or portion thereof) as a hardware module that operates to perform operations described herein for that module.

One or more of the processor circuits discussed herein can perform operations in a “cloud computing” environment or as a service (e.g., within a “software as a service” (SaaS) implementation). For example, at least some operations within any one or more of the methods discussed herein can be performed by a group of computers (e.g., as examples of machines that include processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., an application program interface (API)). The performance of certain operations can be distributed among the one or more processors, whether residing only within a single machine or deployed across a number of machines, for example, in the network100ofFIG. 1. In some example embodiments, the one or more processors or hardware modules (e.g., processor-implemented modules) can be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example embodiments, the one or more processors or hardware modules can be distributed across a number of geographic locations.

Several additional examples follow. Example 1 can include or use subject matter (such as an apparatus, a method, a means for performing operations, or a machine readable memory including instructions that, when performed by the machine, can configure the machine to perform acts), such as can include or use a method for classifying media, the method comprising accessing, using one or more processor circuits associated with a first device, digital media data that represents a media query to be identified, determining, using the one or more processor circuits associated with the first device, a noise characteristic corresponding to the digital media data, and if the determined noise characteristic indicates less than a specified threshold amount of noise corresponding to the digital media data, then transmitting the digital media data to a remote media classification circuit that is configured to identify a source characteristic of the digital media data, and otherwise inhibiting the transmitting the digital media data to the remote media classification circuit.

Example 2 can include or use, or can optionally be combined with the subject matter of Example 1, to further include wherein the accessing the digital media data includes receiving an audio signal using a microphone of a mobile device, and wherein the determining the noise characteristic includes determining a noise characteristic of the received audio signal using the one or more processor circuits.

Example 3 can include or use, or can optionally be combined with the subject matter of Examples 1 or 2, to further include accessing, using the one or more processor circuits associated with the first device, a first context parameter that corresponds to the media query to be identified, and determining, using the one or more processor circuits associated with the first device, a signal quality characteristic corresponding to the first context parameter. In Example 3, if the determined signal quality characteristic corresponding to the first context parameter is less than a specified threshold signal quality, then the Example can include inhibiting the transmitting the digital media data to the remote media classification circuit.

Example 4 can include or use, or can optionally be combined with the subject matter of at least one of Examples 1-3, to further include retrieving the specified threshold amount of noise from a database of threshold noise characteristics, the database established by prior training of the same or similar media queries with a neural network-based classifier system, the database stored at the first device or at the remote media classification circuit.

Example 5 can include or use, or can optionally be combined with the subject matter of at least one of Examples 1-4, to further include wherein the transmitting the digital media data to the media classification circuit includes providing the digital media data to an input of a convolutional neural network classifier system.

Example 6 can include or use, or can optionally be combined with the subject matter of at least one of Examples 1-5, to further include wherein if the determined noise characteristic corresponds to a first specified noise threshold range, then initiating a first media classification process having a first search depth using the media classification circuit, and if the determined noise characteristic corresponds to a second greater noise threshold range, then initiating a second media classification process having a different second search depth using the media classification circuit.

Example 7 can include or use, or can optionally be combined with the subject matter of at least one of Examples 1-6, to further include selecting a media classification search depth based on the determined noise characteristic corresponding to the digital media data, wherein the selected media classification search depth indicates a maximum processing time elapsed or a maximum processing effort expended by the media classification circuit to identify the source characteristic of the digital media data.

Example 8 can include or use, or can optionally be combined with the subject matter of at least one of Examples 1-7, to further include comparing, using the remote media classification circuit, the determined noise characteristic corresponding to the digital media data with noise characteristics corresponding to successfully classified other media data and unsuccessfully classified other media data, and if the determined noise characteristic more closely corresponds to the noise characteristics corresponding to successfully classified other media data than to the noise characteristics corresponding to unsuccessfully classified other media data, then transmitting the digital media data to the media classification circuit.

Example 9 can include or use, or can optionally be combined with the subject matter of at least one of Examples 1-8, to further include wherein the inhibiting the transmitting the digital media data to the media classification circuit includes accessing subsequent digital media data that represents a different media query to be identified, determining an updated noise characteristic corresponding to the subsequent digital media data, and determining whether the updated noise characteristic indicates less than the specified threshold amount of noise.

Example 10 can include or use, or can optionally be combined with the subject matter of at least one of Examples 1-9, to further include wherein the accessing the digital media data includes accessing an audio sample, using the first device, corresponding to the media query, wherein the determining the noise characteristic includes determining a noise characteristic for the audio sample, and wherein the transmitting the digital media data to the media classification circuit includes transmitting all or a portion of the audio sample to the media classification circuit.

Example 11 can include or use, or can optionally be combined with the subject matter of at least one of Examples 1-10, to further include wherein the accessing the digital media data includes accessing a video signal sample corresponding to the media query, wherein the determining the noise characteristic includes determining a visual characteristic of the video signal sample, and wherein the transmitting the digital media data to the media classification circuit includes transmitting all or a portion of the video signal sample to the media classification circuit.

Example 12 can include or use, or can optionally be combined with the subject matter of at least one of Examples 1-11, to further include receiving, at the first device and from the media classification circuit, an indication of a source characteristic of the digital media data, and displaying the indication of the source characteristic of the digital media data to a user of the mobile device.

Example 13 can include or use subject matter (such as an apparatus, a method, a means for performing operations, or a machine readable memory including instructions that, when performed by the machine, can configure the machine to perform acts), such as can include or use a tangible, non-transitory computer-readable storage medium comprising instructions that, when executed by at least one processor of a machine, cause the machine to perform operations comprising: accessing, using one or more processor circuits, digital media data that represents a media query to be identified; determining, using the one or more processor circuits, a likelihood that the media query can be successfully identified by a neural network classifier based on a spectral characteristic of the digital media data; and if the determined likelihood is greater than a specified threshold likelihood, then: providing the digital media data to a first input of the neural network classifier; receiving from the neural network classifier, in response to the digital media data, a media type probability index for the media query; and providing information about the media type probability index to a remote device to provide an indication of a media type to a user of the remote device.

Example 14 can include or use, or can optionally be combined with the subject matter of Example 13, to further include operations that comprise determining a signal noise characteristic corresponding to the digital media data, and wherein the determining the likelihood that the media query can be successfully identified by the neural network classifier includes determining the likelihood based on the determined signal noise characteristic.

Example 15 can include or use, or can optionally be combined with the subject matter of Examples 13 or 14, to further include operations that comprise determining a frequency content characteristic corresponding to the digital media data, and wherein the determining the likelihood that media query can be successfully identified by the neural network classifier includes determining the likelihood based on the determined frequency content characteristic.

Example 16 can include or use, or can optionally be combined with the subject matter of at least one of Examples 13-15, to further include operations that comprise accessing a context parameter associated with the media query, wherein the context parameter and the digital media data are accessed from the remote device, and wherein the determining the likelihood includes determining a likelihood that the media query can be successfully identified by a neural network classifier that is configured based on a network node weighting model, and wherein the weighting model is selected based on the accessed context parameter.

Example 17 can include or use subject matter (such as an apparatus, a method, a means for performing operations, or a machine readable memory including instructions that, when performed by the machine, can configure the machine to perform acts), such as can include or use a system comprising a mobile device comprising a first sensor configured to receive digital media data that represents a media query to be identified and a first processor circuit configured to transmit the digital media data to a remote media classifier circuit. In Example 17, the system can further include a second processor circuit associated with the remote media classifier circuit, the second processor circuit configured to receive the digital media data from the mobile device, and the second processor circuit configured to execute instructions that cause the second processor circuit to perform operations comprising: determining a spectral characteristic corresponding to the digital media data; and based on the determined spectral characteristic corresponding to the digital media data, enabling or inhibiting a convolutional neural network-based media classifier executed at least in part by the second processor circuit and using the digital media data as an input, the convolutional neural network-based media classifier configured to communicate, to the mobile device, an indication of a likelihood that the digital media data corresponds to a specified media class.

Example 18 can include or use, or can optionally be combined with the subject matter of Example 17, to further include wherein the mobile device further includes a second sensor configured to sense first context information that corresponds to the media query to be identified, and wherein the first processor circuit is configured to transmit the digital media data and the first context information to the remote media classifier circuit. In Example 18, when the second processor circuit enables the convolutional neural network-based media classification process, the second processor circuit's operations can further comprise selecting a first classification model for use by the convolutional neural network from a database comprising a plurality of different classification models, the selecting based on the received digital media data and the first context information about the mobile device.

Example 19 can include or use, or can optionally be combined with the subject matter of Examples 17 or 18, to further include wherein the second processor circuit is configured to execute instructions that cause the second processor circuit to perform operations that further comprise: selecting a neural network node-weighting model from a database that includes multiple node-weighting models, the selected model for use by the convolutional neural network-based media classifier to provide the indication of the likelihood that the digital media data corresponds to the specified media class, the selecting the model using the determined spectral characteristic corresponding to the digital media data, wherein the selected model defines a weighting characteristic of one or more nodes in the convolutional neural network-based media classifier.

Example 20 can include or use, or can optionally be combined with the subject matter of at least one of Examples 1-19, to further include wherein the first processor circuit of the mobile device is further configured to identify a signal noise characteristic of the digital media data and to transmit the digital media data to the remote media classifier circuit only when the identified signal noise characteristic indicates less than a specified threshold amount of noise in the digital media data.

In this document, the terms “a” or “an” are used, as is common in patent documents, to include one or more than one, independent of any other instances or usages of “at least one” or “one or more.” In this document, the term “or” is used to refer to a nonexclusive or, such that “A or B” includes “A but not B,” “B but not A,” and “A and B,” unless otherwise indicated. In this document, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.”

Although various general and specific embodiments are described herein, it will be evident that various modifications and changes can be made to these embodiments without departing from the broader spirit and scope of the present disclosure. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. The accompanying drawings that form a part of this application show, by way of illustration, and not of limitation, specific embodiments in which the subject matter can be practiced. The embodiments illustrated are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments can be used or derived there from, such that structural and logical substitutions and changes can be made without departing from the scope of this disclosure. This Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled. Specific embodiments or examples are illustrated and described herein, however, it should be appreciated that any arrangement calculated to achieve the same purpose can be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will be apparent to those of skill in the art upon reviewing the above description.