text: US Patent for Acoustic neural network scene detection Patent (Patent # 10,878,837 issued December 29, 2020) - Justia Patents Search
  Justia Patents Specialized Equations Or ComparisonsUS Patent for Acoustic neural network scene detection Patent (Patent # 10,878,837)
  Acoustic neural network scene detection
  Feb 28, 2018 - Snap Inc.
  An acoustic environment identification system is disclosed that can use neural networks to accurately identify environments. The acoustic environment identification system can use one or more convolutional neural networks to generate audio feature data. A recursive neural network can process the audio feature data to generate characterization data. The characterization data can be modified using a weighting system that weights signature data items. Classification neural networks can be used to generate a classification of an environment.
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  This application claims priority to U.S. Provisional Application Ser. No. 62/465,550, filed Mar. 1, 2017, the disclosure of which is incorporated herein in its entirety by reference.
  Embodiments of the present disclosure relate generally to environment recognition and, more particularly, but not by way of limitation, to acoustic based environment classification using neural networks.
  Audio sounds carry a large amount of information about our everyday environment and physical events that take place in it. Having a machine that understands the environment, e.g., through acoustic events inside the recording, is important for many applications such as security surveillance, context-aware services, and video scene identification combined with image scene detection. However, identifying the location in which a specific audio file was recorded, e.g., a beach or on a bus, is a challenging task for a machine. The complex sound composition of real life audio recordings makes it difficult to obtain representative features for recognition.
  FIG. 1 shows a block diagram illustrating a networked system for environment recognition, according to some example embodiments.
  FIG. 2 shows a block diagram showing example components provided within the environment recognition system of FIG. 1, according to some example embodiments.
  FIG. 3 shows a flow diagram of a method for performing acoustic based recognition of environments, according to some example embodiments.
  FIG. 4 shows an architecture diagram for implementing acoustic based environment classification using neural networks, according to some example embodiments.
  FIG. 5 shows an example architecture of a combination layer having a forward-based neural network, according to some example embodiments.
  FIG. 6 shows an example architecture of a combination layer having a backward-based neural network, according to some example embodiments.
  FIG. 7 shows an example architecture of a combination layer having a bidirectional-based neural network, according to some example embodiments.
  FIG. 8 shows an example configuration of a bidirectional network of the characterization layer, according to some example embodiments.
  FIG. 9 illustrates a flow diagram for a method for training the deep learning group, according to some example embodiments.
  FIG. 10 shows an example user interface of client device implementing acoustic based scene detection, according to some example embodiments.
  FIG. 11 illustrates a diagrammatic representation of a machine in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein, according to an example embodiment.
  Audio sounds carry information about our everyday environment and physical events that take place in it. Having a machine that understands the environment, e.g., through acoustic events inside the recording, is important for many applications such as security surveillance, context-aware services and video scene identification by combining with image scene detection. However, identifying the location in which a specific audio file was recorded, e.g., at a beach or on a bus, is a challenging task for a machine. The complex sound composition of real life audio recordings makes it difficult to obtain representative features for recognition. Compounding the problem, with the rise of social media networks, many available audio samples are extremely short in duration (e.g., ˜6 seconds of audio from a short video clip) and using a machine to detect a scene type may be extremely difficult because short samples have fewer audio clues of scene type.
  To address these issues, an audio deep identification (ID) system implements a novel framework of neural networks to more accurately classify an environment using acoustic data from the environment. In some example embodiments, three types of neural network layers are implemented, including a convolution layer, a recursive layer, e.g., a bidirectional long short-term memory (LSTM) layer, and a fully connected layer. The convolution layer extracts the hierarchical feature representation from acoustic data recorded from an environment. The recursive layer models the sequential information, including the feature representation data generated from the convolution layer. The fully connected layer classifies the environment using the sequential information generated from the recursive layer. In this way, the acoustic data from a given environment can be efficiently and accurately identified.
  Furthermore, according to some example embodiments, an attention mechanism can be implemented to weight the hidden states of each time step in the recursive layer to predict the importance of the corresponding hidden states, as discussed in further detail below. In some example embodiments, the weighted sum of the hidden states are used as input for the next layers (e.g., the classification layers). The recursive vector (e.g., LSTM hidden state vector) and the attention vector (e.g., the vector generated by the attention mechanism) exhibit different and complementary data. In some example embodiments, the two vectors are combined and used to continually train the attention mechanism and the recursive layer. The combination of the two vectors in training synergistically characterizes the acoustic data to produce significantly more accurate acoustic classifications.
  With reference to FIG. 1, an example embodiment of a high-level client-server-based network architecture 100 is shown. A networked system 102, in the example forms of a network-based marketplace or payment system, provides server-side functionality via a network 104 (e.g., the Internet or a wide area network (WAN)) to one or more client devices 110. In some implementations, a user (e.g., user 106) interacts with the networked system 102 using the client device 110. FIG. 1 illustrates, for example, a web client 112 (e.g., a browser), applications 114, and a programmatic client 116 executing on the client device 110. The client device 110 includes the web client 112, the client application 114, and the programmatic client 116 alone, together, or in any suitable combination. Although FIG. 1 shows one client device 110, in other implementations, the network architecture 100 comprises multiple client devices.
  The client device 110 communicates with the network 104 via a wired or wireless connection. For example, one or more portions of the network 104 comprises an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a WAN, a wireless WAN (WWAN), a metropolitan area network (MAN), a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a cellular telephone network, a wireless network, a wireless fidelity (WI-FI®) network, a Worldwide Interoperability for Microwave Access (WiMax) network, another type of network, or any suitable combination thereof.
  In some example embodiments, the client device 110 includes one or more of the applications 114 (also referred to as "apps") such as, but not limited to, web browsers, book reader apps (operable to read e-books), media apps (operable to present various media forms including audio and video), fitness apps, biometric monitoring apps, messaging apps, and electronic mail (email) apps. In some implementations, the client application 114 include various components operable to present information to the user 106 and communicate with networked system 102.
  The web client 112 accesses the various systems of the networked system 102 via the web interface supported by a web server 122. Similarly, the programmatic client 116 and client application 114 access the various services and functions provided by the networked system 102 via the programmatic interface provided by an application program interface (API) server 120.
  Users (e.g., the user 106) comprise a person, a machine, or other means of interacting with the client device 110. In some example embodiments, the user 106 is not part of the network architecture 100, but interacts with the network architecture 100 via the client device 110 or another means. For instance, the user 106 provides input (e.g., touch screen input or alphanumeric input) to the client device 110 and the input is communicated to the networked system 102 via the network 104. In this instance, the networked system 102, in response to receiving the input from the user 106, communicates information to the client device 110 via the network 104 to be presented to the user 106. In this way, the user 106 can interact with the networked system 102 using the client device 110.
  The API server 120 and the web server 122 are coupled to, and provide programmatic and web interfaces respectively to, one or more application servers 140. The application server 140 can host an audio deep ID system 150, which can comprise one or more modules or applications 114, each of which can be embodied as hardware, software, firmware, or any combination thereof. The application server 140 is, in turn, shown to be coupled to a database server 124 that facilitates access to one or more information storage repositories, such as database 126. In an example embodiment, the database 126 comprises one or more storage devices that store information to be accessed by audio deep ID system 150 or client device 110. Additionally, a third party application 132, executing on third party server 130, is shown as having programmatic access to the networked system 102 via the programmatic interface provided by the API server 120. For example, the third party application 132, utilizing information retrieved from the networked system 102, supports one or more features or functions on a website hosted by the third party. While audio deep ID system 150 is illustrated as executing from application server 140 in FIG. 1, in some example embodiments, audio deep ID system 150 is installed as part of application 114 and run from client device 110, as is appreciated by those of ordinary skill in the art.
  Further, while the client-server-based network architecture 100 shown in FIG. 1 employs a client-server architecture, the present inventive subject matter is, of course, not limited to such an architecture, and can equally well find application in a distributed, or peer-to-peer, architecture system, for example. The various systems of the application server 140 (e.g., the audio deep ID system 150) can also be implemented as standalone software programs, which do not necessarily have networking capabilities.
  FIG. 2 illustrates a block diagram showing functional components provided within the audio deep ID system 150, according to some embodiments. In various example embodiments, the audio deep ID system 150 comprises an input engine 205, a deep learning group 210, an output engine 240, a training engine 245, and a database engine 250. Further, according to some example embodiments, the deep learning group 210 comprises one or more engines including a convolutional engine 215, a recursive engine 220, an attention engine 225, a combination engine 230, and a classification engine 235. The components themselves are communicatively coupled (e.g., via appropriate interfaces) to each other and to various data sources, so as to allow information to be passed between the components or so as to allow the components to share and access common data. Furthermore, the components access the database 126 via the database server 124 and the database engine 250.
  The input engine 205 manages generating the audio data for a given environment, according to some example embodiments. The input engine 205 may implement a transducer, e.g., a microphone, to record the environment to create audio data. The audio data can be in different formats including visual formats (e.g., a spectrogram).
  The deep learning group 210 is a collection of one or more engines that perform classification of the environment based on the audio data from the environment. In some example embodiments, the deep learning group 210 implements a collection of artificial neural networks to perform the classification on the environment, as discussed in further detail below.
  The training engine 245 is configured to train the deep learning group 210 using audio training data recorded from different environments. In some example embodiments, the audio training data is prerecorded audio data of different environments, such as a restaurant, a park, or an office. The training engine 245 uses the audio training data to maximize or improve the likelihood of a correct classification using neural network training techniques, such as back propagation.
  The database engine 250 is configured to interface with the database server 124 to store and retrieve the audio training data in the database 126. For example, the database server 124 may be an SAP® database server or an Oracle® database server, and the database engine 250 is configured to interface with the different types of database servers 124 using different kinds of database code (e.g., Oracle SQL for Oracle® server, ABAP for SAP® servers) per implementation.
  The output engine 240 is configured to receive a classification from the deep learning group 210 and use it for different tasks such as security surveillance, context-aware services, and video scene identification. For example, a user 106 may record a video stream using his/her camera and microphone of his/her client device 110. The audio data from the video stream can be used by the deep learning group 210 to determine that the user 106 is in a restaurant. Based on the environment being classified as restaurant, the output engine 240 may provide the user 106 an option to overlay cartoon forks and knives over his/her video stream. The modified video stream may then be posted to social network for viewing by other users on their own respective client devices 110.
  FIG. 3 shows a flow diagram of a method 300 detecting a scene using a neural network, according to some example embodiments. At operation 310, the input engine 205 generates audio data from the environment. For example, the input engine 205 uses a transducer, e.g., a microphone, to record acoustic data from the environment. The acoustic data may be initially in an audio format such as Waveform Audio File Format (WAV). The acoustic data may be accompanied by one or more frames of a live video being displayed or recorded on client device 110. The input engine 205 then converts the acoustic data into audio data in a visual format, e.g., a spectrogram, for further analysis by the deep learning group 210, according to some example embodiments.
  Operations 320-340 are performed by the functional engines of the deep learning group 210. At operation 320, the convolutional engine 215 processes the audio data to generate audio feature data items that describe audio features (e.g., sounds such as a knife being dropped, or a school bus horn) of the environment. In some example embodiments, the convolutional engine 215 implements a convolutional neural network that uses the audio data to generate the audio feature data as vector data that can be processed in one or more hidden layers the neural network architecture.
  At operation 330, the recursive engine 220 processes the audio feature data to generate characterization data items. In some example embodiments, the recursive engine 220 implements a recursive neural network (e.g., an LSTM) that ingests the audio feature data to output the characterization data as vector data. At operation 340, the classification engine 235 processes the characterization data to generate classification data for the environment.
  The classification data may include a numerical quantity from 0 to 1 that indicates the likelihood that the environment is of a given scene type, e.g., a restaurant, a street, or a classroom. For example, the classification data may generate a score of 0.45 for the restaurant scene probability, a score of 0.66 for the street scene probability, and a classroom scene probability of 0.89. In some example embodiments, the classification engine 235 classifies the environment as the scene type having the highest scene probability. Thus, in the example above, the recorded environment is classified as a classroom scene because the classroom scene has the highest scene probability.
  At operation 350, based on the classification, the output engine 240 selects one or more items of content for overlay on one or more images of the live video feed. For example, if the environment is classified as a classroom, the overlay content may include a cartoon chalkboard and a cartoon apple, or a location tag (e.g., "Santa Monica High School") as overlay content on the live video feed, which shows images or video of a classroom. At operation 360, the output engine 240 publishes the one or more frames of the live video feed with the overlay content as an ephemeral message of a social media network site (e.g., a website, a mobile app). An ephemeral message is a message that other users 106 of the social media network site can temporarily access for a pre-specified amount of time (e.g., 1 hour, 24 hours). Upon expiry of the pre-specified amount of time, the ephemeral message expires and is made inaccessible to users 106 of the social media network site.
  FIG. 4 illustrates an architecture diagram of a flow 400 for implementing acoustic based environment classification using neural networks, according to some example embodiments. Each of the solid line blocks is a functional layer that implements a mechanism, such as a neural network. The arrows between the functional layers of FIG. 4 represent the output of data from one functional layer into the next functional layer (e.g., the audio input layer 405 generates a data output that is input into the first convolutional layer 410). The flow 400 is an example embodiment of the method 300 in FIG. 3 in that audio data is the initial input and the output is a scene classification.
  According to some example embodiments, the initial layer is the audio input layer 405. The audio input layer 405 implements a transducer to record an environment and generate audio data for analysis. For example, the audio input layer 405 may use a microphone of the client device 110 to record the surrounding environment. The audio data is then conveyed to the first convolutional layer 410, and then to the second convolutional layer 415. The convolutional layers are convolutional neural networks configured to extract features from the audio data at different scales. For example, the first convolutional layer 410 may be configured to generate feature data of large audio features at a first scale (e.g., loud or longer noises), while the second convolutional layer 415 is configured to generate feature data of small audio features at a second, smaller scale (e.g., short, subtle noises). In the example illustrated in FIG. 4, the first convolutional layer 410 receives the audio data as an input from audio input layer 405. The first convolutional layer 410 then processes the audio data to generate vector data having audio features described at a larger scale. The vector data having audio features described at a larger scale is then input into the second convolutional layer 415. The second convolutional layer 415 receives the data and generates audio feature data that describes features at a smaller scale.
  The output data from the second convolutional layer 415 is then input into a characterization multi-layer 420, according to some example embodiments. As illustrated, the characterization multi-layer 420 comprises a characterization layer 425 and an attention layer 430. The characterization layer 425 uses a neural network to implement time sequential modeling of the audio feature data. That is, the characterization layer 425 uses a neural network that summarizes the audio feature data temporally. In some example embodiments, the characterization layer 425 is a bi-directional LSTM neural network that takes into account the forward time direction (e.g., from the start of the recorded audio data to the end of the recorded audio data) in the audio data and reverse time direction (e.g., from the end of the recorded audio data to the start of the recorded audio data). The bi-directional LSTM processes the data recursively over a plurality of hidden time steps, as is understood by those having ordinary skill in the art of artificial neural networks. In some embodiments, the last output from the second convolutional layer 415 is reshaped into a sequence of vectors before being input into the characterization layer 425. Each vector of the sequence represents a sound feature extracted for a given time step in an LSTM in the characterization layer 425. Further, the bi-directional LSTM concatenates the final state of the forward direction and the final state of the reverse direction to generate the output of characterization layer 425. Other types of LSTMs can also be implemented in the characterization layer 425, such as a forward LSTM or backward LSTM, as discussed below.
  The attention layer 430 is a fully connected neural network layer that is parallel to the characterization layer 425, according to some example embodiments. During training, each scene may have signature sounds that are strongly associated with a given type of environment. For example, dishes dropping and forks and knives clacking together may be signature sounds of a restaurant. In contrast, some sounds or lack thereof (e.g., silence) do not help identify which environment is which. The attention layer 430 is trained (e.g., via back propagation) to more heavily weight hidden state time steps that contain signature sounds. In some example embodiments, the attention layer 430 is further configured to attenuate the value of the hidden state time steps that do not contain signature sounds. For example, if a hidden state time step contains audio data of dishes clacking together, the attention layer 430 will more heavily weight the vector of the hidden state time step. In contrast, if a hidden state time step is a time step containing only silence or subtle sounds, the attention layer 430 will decrease the value of the vector of the hidden state time step.
  According to some example embodiments, the attention layer 430 weights each hidden state of the bi-directional LSTM to generate a weighted sum. In particular, the attention layer 430 uses each hidden state time stamp vector of the bi-directional LSTM to weight itself (e.g., amplify or diminish its own value), thereby predicting its own value. For a given time step, once the weighting is generated, it may be combined with the vector of the LSTM time step through multiplication, as discussed in further detail below.
  The attention layer 430 may use different schemes to produce weightings. In one example embodiment, each hidden state predicts its own weight using a fully connected layer. That is, the vector of a hidden state of the LSTM in the characterization layer 425 is input into the fully connected layer in the attention layer 430, which generates a predicted weighting. The predicted weighting is then combined with the vector of the hidden state through multiplication.
  In another example embodiment, all hidden states of the LSTM work together, in concert, to predict all weights for each of the time steps using an individual fully connected layer. This approach takes into account information of the entire sequence (from beginning of audio data to the end) to predict weights for each time step.
  In yet another example embodiment, all hidden states of the LSTM work together, using a convolution layer that feeds into a max pooling layer. This approach retrieves time local information (e.g., vector data for a time step) and extracts the features to predict all weights.
  The combination layer 435 is configured to combine the data received from the characterization layer 425 and the attention layer 430, according to some example embodiments. FIGS. 5-8 illustrate various example architectures for the characterization layer 425 and combination layer 435, according to some example embodiments. In some example embodiments, the output from the combination layer 435 is input into a fully connected layer 440. Generally, the fully connected layer 440 and the classification layer 445 manage classification of the audio data to generate scene classification scores. The fully connected layer 440 implements a fully connected neural network to modify the data received form the combination layer 435 according to how the fully connected neural network is weighted during training. Training of the different layers is discussed in further detail in FIG. 6. The data output of the fully connected layer 440 is then input into a classification layer 445. In some embodiments, the classification layer 445 implements a SoftMax neural network to generate classification scores for the recorded audio data of the environment. In some example embodiments, the scene having the highest classification score is selected as the scene of the environment. The output layer 450 receives information on which scene scores the highest and is thus selected as the scene of the environment. The output layer 450 can use then the identified scene in further processes. For example, if the highest scoring scene is restaurant, the output layer 450 may overlay images (e.g., fork and knife) over video data of the recorded environment.
  As mentioned above, FIGS. 5-8 illustrate different example architectures for the combination layer 435, according to some example embodiments. As illustrated in FIG. 5, characterization layer 425 is a forward LSTM that is trained and processes audio data in a forward time direction. In some example embodiments, the forward LSTM characterization layer 425 uses 256 hidden nodes and the output of the final time setup is input into the combination layer 435 for merging with the attention layer 430 output data (e.g., weighted vectors). The combination layer 435 comprises a concatenation layer 505, a fully connected layer 510, and a SoftMax layer 515. According to some example embodiments, the concatenation layer 505 combines by concatenating the forward LSTM data with the attention data. The output of the concatenation layer 505 is output into the fully connected layer 510 for processing, which in turn transmits its data to a SoftMax layer 515 for classification. Optimization (e.g., backpropagation, network training) may occur over the fully connected layer 510 and the SoftMax layer 515 to maximize the likelihood of generating an accurate summary vector (e.g., enhanced characterization data). The output of the combination layer 435 may then be fed into an output layer, such as fully connected layer 440, as discussed above.
  In some example embodiments, as illustrated in FIG. 6, the characterization layer 425 is a backwards LSTM that processes audio data in a backwards time direction. The backward LSTM characterization layer 425 uses 256 hidden nodes and the output of the final time setup is input into the combination layer 435 for merging with the attention layer 430 output data (e.g., weighted vectors). Further, in some embodiments, the backwards LSTM characterization layer 425 inputs data into a first fully connected layer 600 and the attention layer 430 inputs its data into a second fully connected layer 605. The outputs of the fully connected layers 600 and 605 are then concatenated in concatenation layer 610, which is then input into a SoftMax layer 615 for optimization (e.g., maximizing likelihood of correct result in training). The output of the combination layer 435 may then be fed into an output layer, such as fully connected layer 440.
  In some example embodiments, as illustrated in FIG. 7, the characterization layer 425 is implemented as a bidirectional LSTM that generates characterization data by processing audio data in both the forward and backward time directions. The bidirectional LSTM characterization layer 425 can concatenate outputs of the final time steps of the forward direction and backward direction, and pass the concatenated data to the combination layer 435. Further details of the bidirectional LSTM characterization layer 425 are discussed below with reference to FIG. 8. As illustrated in FIG. 7, data outputs from the bidirectional LSTM characterization layer 425 (e.g., the concatenated final and hidden states of both time directions) and the attention layer 430 (e.g., weighted vectors) are input into the combination layer 435. In particular, the characterization layer 425 inputs data into a first fully connected layer 700 and the attention layer 430 inputs its data into a second fully connected layer 705. The first fully connected layer 700 then inputs into a first SoftMax layer 710 and the second fully connected layer 705 inputs into a second SoftMax layer 715. The outputs of the SoftMax layers 710 and 715 are input into a linear combination layer 720. In the linear combination layer 720, each of the SoftMax layer inputs receive a weighting. For example, the output from the first SoftMax layer 710 is weighted by weight scalar "W1" and the output from the second SoftMax layer 715 is weighted by the weight scalar "W2". The linear combination layer 720 then adjusts the weight scalars "W1" and "W2" to maximize the likelihood of an accurate output.
  FIG. 8 shows an example configuration 800 of a bidirectional LTSM of the characterization layer 425, according to some example embodiments. As discussed, the bidirectional LTSM of the characterization layer 425 can concatenate the final steps in both time directions and pass it to the next layer, e.g., a layer that combines attention weights. The final or last output of the final time step in an LSTM summarizes all previous time steps' data for a given direction. As mentioned, some time steps may have information (e.g., audio data of dishes clanging together) that is more indicative of a given type of scene (e.g., a cafe or restaurant). Attention weights can emphasize time steps that correspond more to a certain type of scene. Analytically, and with reference to configuration 800 in FIG. 8, let h(t) denote the hidden state of each LSTM time step with length T. The bidirectional LSTM is configured with a mapping function ƒ(.) that uses hidden states to predict an attention score/weight watt for each time step. The final output Oatt is the normalized weighted sum of all the hidden states as shown in configuration 800 (each time step contributes, in each direction). Equations 1-3 describe the time steps of the bidirectional LSTM being combined with attention scores (e.g., "w_att1", "w_att2", "w_att3"), according to some example embodiments. A Softmax function can used to normalize the score, and the output of the Softmax function represents a probabilistic interpretation of the attention scores.
  watt=ƒ(h(t)) [Ex. 1]
  wattnorm=ƒ(h(t)) [Ex. 2]
  O a ⁢ t ⁢ t = ∑ t = 1 T ⁢ h ⁡ ( t ) * w att norm [ Ex . ⁢ 3 ]
  The mapping function f(.) can be implemented as a shallow neural network with a single fully connected layer and a linear output layer. In some example embodiments, each hidden state predicts its own weight, which is a one-to-one mapping. Further, in some example embodiments, all the hidden states are used together to predict all the weights for each time step, which is an all-to-all mapping. In some embodiments, hidden states of the same time step from both forward and backward directions are concatenated to represent h(t). For example, in the first time step, h1 in the forward direction (which has an arrow leading to h2) is concatenated with h1 in the backward direction.
  FIG. 9 illustrates a flow diagram for a method 900 for training the deep learning group 210, according to some example embodiments. At operation 905, the training engine 245 inputs the training data into the deep learning group 210 for classification. For example, the training engine 245 instructs the database engine 250 to retrieve the training data from the database 126 via database server 124. In some example embodiments, the training data comprises audio data recorded from fifteen or more scenes, including, for example, audio data recorded from a bus, a cafe, a car, a city center, a forest, a grocery store, a home, a lakeside beach, a library, a railway station, an office, a residential area, a train, a tram, and an urban park.
  At operation 910, the deep learning group 210 receives the training audio data for a given scene and generates classification data. In some example embodiments, for a set of training audio data, each scene receives a numerical classification score, as discussed above.
  At operation 915, the training engine 245 compares the generated classifications to the known correct scene to determine if the error exceeds a pre-determined threshold. For example, if the training audio data is a recording of a restaurant, then the known correct scene should be "restaurant," which will be indicated as correct by having the highest numerical score. If the generated classification scores do not give the highest numerical score to the correct scene, then the valid response is unlikely (e.g., the maximum likelihood is not achieved) and the process continues to operation 920, where the training engine 245 adjusts the neural networks (e.g., calculates gradient parameters to adjust the weightings of the neural network) to maximize likelihood of a valid response. The process may loop to operation 910 until the correct scene receives the highest classification score. When the correct scene receives the highest score, the method continues to operation 925, where the system is set into output mode. In output mode, the neural networks of the deep learning group 210 are trained and ready to receive audio records and generate classifications.
  FIG. 10 shows an example user interface 1000 of client device 110 implementing acoustic based scene detection, according to some example embodiments. In FIG. 10, the user 106 has walked into a coffee shop and used client device 110 to record approximately six seconds of video of the coffee shop. Method 300, discussed above, can then be performed on the client device 110 or on the server 140 to classify the type of scene based on audio data and overlay content. For example, in FIG. 10, the user 106 is in a coffee shop, thus there may be sounds of people talking and dishes clashing. The audio deep ID system 150 can then determine that the user 106 is in a coffee shop and select overlay content, such as user avatar 1010 and location content 1005 for overlay on the video of the coffee shop. One or more frames (e.g., an image or the full six seconds of video) can then be published to a social media network site as an ephemeral message.
  Certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. Modules can constitute either software modules (e.g., code embodied on a machine-readable medium) or hardware modules. A "hardware module" is a tangible unit 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 (e.g., a standalone computer system, a client computer system, or a server computer system) or one or more hardware modules of a computer system (e.g., a processor or a group of processors) can be configured by software (e.g., an application 114 or application portion) as a hardware module that operates to perform certain operations, as described herein.
  Similarly, the methods described herein can be at least partially processor-implemented, with a particular processor or processors being an example of hardware. For example, at least some of the operations of a method can be performed by one or more processors or processor-implemented modules. Moreover, the one or more processors may also operate to support performance of the relevant operations in a "cloud computing" environment or as a "software as a service" (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), with these operations being accessible via a network 104 (e.g., the Internet) and via one or more appropriate interfaces (e.g., an API).
  FIG. 11 is a block diagram illustrating components of a machine 1100, according to some example embodiments, able to read instructions from a machine-readable medium (e.g., a machine-readable storage medium) and perform any one or more of the methodologies discussed herein. Specifically, FIG. 11 shows a diagrammatic representation of the machine 1100 in the example form of a computer system, within which instructions 1116 (e.g., software, a program, an application 114, an applet, an app, or other executable code) for causing the machine 1100 to perform any one or more of the methodologies discussed herein can be executed. For example, the instructions 1116 can cause the machine 1100 to execute the diagrams of FIGS. 3-6. Additionally, or alternatively, the instruction 1116 can implement the input engine 205, the deep learning group 210, the output engine 240, the training engine 245, the database engine 250, the convolutional engine 215, the recursive engine 220, the attention engine 225, the combination engine 230, and the classification engine 235 of FIG. 2, and so forth. The instructions 1116 transform the general, non-programmed machine into a particular machine programmed to carry out the described and illustrated functions in the manner described. In alternative embodiments, the machine 1100 operates as a standalone device or can be coupled (e.g., networked) to other machines. In a networked deployment, the machine 1100 may 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 peer-to-peer (or distributed) network environment. The machine 1100 can comprise, but not be limited to, a server computer, a client computer, a PC, a tablet computer, a laptop computer, a netbook, a set-top box (STB), a PDA, an entertainment media system, a cellular telephone, a smart phone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions 1116, sequentially or otherwise, that specify actions to be taken by the machine 1100. Further, while only a single machine 1100 is illustrated, the term "machine" shall also be taken to include a collection of machines 1100 that individually or jointly execute the instructions 1116 to perform any one or more of the methodologies discussed herein.
  The machine 1100 can include processors 1110, memory/storage 1130, and input/output (I/O) components 1150, which can be configured to communicate with each other such as via a bus 1102. In an example embodiment, the processors 1110 (e.g., a central processing unit (CPU), a reduced instruction set computing (RISC) processor, a complex instruction set computing (CISC) processor, a graphics processing unit (GPU), a digital signal processor (DSP), an ASIC, a radio-frequency integrated circuit (RFIC), another processor, or any suitable combination thereof) can include, for example, processor 1112 and processor 1114 that may execute instructions 1116. The term "processor" is intended to include multi-core processors that may comprise two or more independent processors (sometimes referred to as "cores") that can execute instructions 1116 contemporaneously. Although FIG. 11 shows multiple processors 1110, the machine 1100 may include a single processor with a single core, a single processor with multiple cores (e.g., a multi-core processor), multiple processors with a single core, multiple processors with multiples cores, or any combination thereof.
  The memory/storage 1130 can include a memory 1132, such as a main memory, or other memory storage, and a storage unit 1136, both accessible to the processors 1110 such as via the bus 1102. The storage unit 1136 and memory 1132 store the instructions 1116 embodying any one or more of the methodologies or functions described herein. The instructions 1116 can also reside, completely or partially, within the memory 1132, within the storage unit 1136, within at least one of the processors 1110 (e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine 1100. Accordingly, the memory 1132, the storage unit 1136, and the memory of the processors 1110 are examples of machine-readable media.
  As used herein, the term "machine-readable medium" means a device able to store instructions 1116 and data temporarily or permanently and may include, but is not be limited to, random-access memory (RAM), read-only memory (ROM), buffer memory, flash memory, optical media, magnetic media, cache memory, other types of storage (e.g., erasable programmable read-only memory (EEPROM)) or any suitable combination thereof. 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 1116. The term "machine-readable medium" shall also be taken to include any medium, or combination of multiple media, that is capable of storing instructions (e.g., instructions 1116) for execution by a machine (e.g., machine 1100), such that the instructions 1116, when executed by one or more processors of the machine 1100 (e.g., processors 1110), cause the machine 1100 to perform any one or more of the methodologies described herein. 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" excludes signals per se.
  The I/O components 1150 can include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components 1150 that are included in a particular machine 1100 will depend on the type of machine 1100. For example, portable machines such as mobile phones will likely include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components 1150 can include many other components that are not shown in FIG. 11. The I/O components 1150 are grouped according to functionality merely for simplifying the following discussion, and the grouping is in no way limiting. In various example embodiments, the I/O components 1150 can include output components 1152 and input components 1154. The output components 1152 can include visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. The input components 1154 can include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or other pointing instruments), tactile input components (e.g., a physical button, a touch screen that provides location and force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like.
  In further example embodiments, the I/O components 1150 can include biometric components 1156, motion components 1158, environmental components 1160, or position components 1162 among a wide array of other components. For example, the biometric components 1156 can include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram based identification), and the like. The motion components 1158 can include acceleration sensor components (e.g., an accelerometer), gravitation sensor components, rotation sensor components (e.g., a gyroscope), and so forth. The environmental components 1160 can include, for example, illumination sensor components (e.g., a photometer), temperature sensor components (e.g., one or more thermometers that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., a barometer), acoustic sensor components (e.g., one or more microphones that detect background noise), proximity sensor components (e.g., infrared sensors that detect nearby objects), gas sensor components (e.g., machine olfaction detection sensors, gas detection sensors to detect concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment. The position components 1162 can include location sensor components (e.g., a GPS receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like.
  Communication can be implemented using a wide variety of technologies. The I/O components 1150 may include communication components 1164 operable to couple the machine 1100 to a network 1180 or devices 11100 via a coupling 1182 and a coupling 11102, respectively. For example, the communication components 1164 include a network interface component or other suitable device to interface with the network 1180. In further examples, communication components 1164 include wired communication components, wireless communication components, cellular communication components, near field communication (NFC) components, BLUETOOTH® components (e.g., BLUETOOTH® Low Energy), WI-FI® components, and other communication components to provide communication via other modalities. The devices 11100 may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a Universal Serial Bus (USB)).
  Moreover, the communication components 1164 can detect identifiers or include components operable to detect identifiers. For example, the communication components 1164 can include radio frequency identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional bar codes such as a Universal Product Code (UPC) bar code, multi-dimensional bar codes such as a Quick Response (QR) code, Aztec Code, Data Matrix, Dataglyph, MaxiCode, PDF4111, Ultra Code, Uniform Commercial Code Reduced Space Symbology (UCC RSS)-2D bar codes, and other optical codes), acoustic detection components (e.g., microphones to identify tagged audio signals), or any suitable combination thereof. In addition, a variety of information can be derived via the communication components 1164, such as location via Internet Protocol (IP) geo-location, location via WI-FI® signal triangulation, location via detecting a BLUETOOTH® or NFC beacon signal that may indicate a particular location, and so forth.
  In various example embodiments, one or more portions of the network 1180 can be an ad hoc network, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, the Internet, a portion of the Internet, a portion of the PSTN, a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a WI-FI® network, another type of network, or a combination of two or more such networks. For example, the network 1180 or a portion of the network 1180 may include a wireless or cellular network, and the coupling 1182 may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or other type of cellular or wireless coupling. In this example, the coupling 1182 can implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3GPP) including 3G, fourth generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) standard, others defined by various standard setting organizations, other long range protocols, or other data transfer technology.
  The instructions 1116 can be transmitted or received over the network 1180 using a transmission medium via a network interface device (e.g., a network interface component included in the communication components 1164) and utilizing any one of a number of well-known transfer protocols (e.g., Hypertext Transfer Protocol (HTTP)). Similarly, the instructions 1116 can be transmitted or received using a transmission medium via the coupling 1172 (e.g., a peer-to-peer coupling) to devices 1170. The term "transmission medium" shall be taken to include any intangible medium that is capable of storing, encoding, or carrying the instructions 1116 for execution by the machine 1100, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software.
  identifying, on a device, sound recording spectrogram data of a physical environment;
  generating, using a convolutional neural network layer of an acoustic classification neural network, sound recording feature data from the sound recording spectrogram data, the acoustic classification neural network comprising the convolutional neural network layer that inputs to a bi-directional long short-term memory (LSTM) neural network layer and an attention neural network layer, the bi-directional LSTM neural network layer and the attention neural network layer configured to input to a deep neural network layer to generate acoustic classifications;
  generating, by the bi-directional LSTM neural network layer, sound recording characterization data for each of a plurality of LSTM time steps by recursively processing each sound recording feature data-for each LSTM time step using the bi-directional LSTM neural network layer;
  generating, using an attention fully connected neural network layer, attention data for each of the plurality of LSTM time steps, the attention data generated by inputting the sound recording feature data from the convolutional neural network layer into the attention neural network layer;
  generating enhanced sound recording characterization data by combining, for each LSTM time step, the sound recording characterization data with the attention data;
  generating, using a deep neural network layer of the acoustic classification neural network, acoustic classification data of the physical environment, the acoustic classification data generated by inputting the enhanced sound recording characterization data; and
  storing the acoustic classification data on the device.
  2. The method of claim 1, wherein the convolutional neural network layer, the bi-directional LSTM neural network layer, the attention neural network layer, and the deep neural network layer are trained as acoustic classification neural network using backpropagation.
  3. The method of claim 1, wherein the attention neural network layer is a fully connected neural network layer.
  4. The method of claim 1, further comprising: generating an audio recording of the physical environment using a sound sensor.
  5. The method of claim 4, further comprising: converting the audio recording into the sound recording spectrogram data of the physical environment.
  6. The method of claim 5, wherein the audio recording is in a non-visual image format and the sound recording spectrogram data is a visual image format.
  training the convolutional neural network layer, the bi-directional LSTM neural network layer, and the deep neural network layer as a network using training audio data recorded from one or more different environments including at least an outdoor environment and an indoor environment.
  8. The method of claim 1, wherein the classification classifies the physical environment into one or more scene categories including an outdoor environment and an indoor environment.
  9. The method of claim 8, wherein the deep neural network layer is a fully connected neural network layer that generates the classification using a classification layer.
  10. The method of claim 9, wherein the classification layer outputs a numerical score for each of the one or more scene categories, the numerical score indicating a likelihood that the physical environment is of a given scene category from the one or more scene categories.
  11. The method of claim 8, wherein the one or more scene categories include one or more of a group comprising: a bus, a cafe, a car, a city center, a forest, a grocery store, a home, a lakeside beach, a library, a railway station, an office, a residential area, a train, a tram, and an urban park.
  12. The method of claim 1, wherein the sound recording spectrogram data is a spectrogram comprising frequency on a vertical axis and time on a horizontal axis.
  a memory comprising instructions that, when executed by the one or more processors, cause the machine to perform operations comprising:
  identifying sound recording spectrogram data of a physical environment;
  storing the acoustic classification data.
  14. The system of claim 13, wherein the convolutional neural network layer, the bi-directional LSTM neural network layer, the attention neural network layer, and the deep neural network layer are trained as acoustic classification neural network using backpropagation.
  15. The system of claim 14, wherein the attention neural network layer is a fully connected neural network layer.
  16. The system of claim 13, the operations further comprising: generating an audio recording of the physical environment using a sound sensor.
  converting the audio recording into the sound recording spectrogram data of the physical environment.
  18. The system of claim 17, wherein the audio recording is in a non-visual image format and the sound recording spectrogram data is a visual image format.
  20. The system of claim 13, wherein the classification classifies the physical environment into one or more scene categories including an outdoor environment and an indoor environment.
  21. The system of claim 20, wherein the deep neural network layer is a fully connected neural network layer that generates the classification using a classification layer.
  22. The system of claim 21, wherein the classification layer outputs a numerical score for each of the one or more scene categories, the numerical score indicating a likelihood that the physical environment is of a given scene category from the one or more scene categories.
  23. The system of claim 20, wherein the one or more scene categories include one or more of a group comprising: a bus, a cafe, a car, a city center, a forest, a grocery store, a home, a lakeside beach, a library, a railway station, an office, a residential area, a train, a tram, and an urban park.
  24. The system of claim 13, wherein the sound recording spectrogram data is a spectrogram comprising frequency on a vertical axis and time on a horizontal axis.
  25. A non-transitory computer readable storage medium comprising instructions that, when executed by one or more processors of a device, cause the device to perform operations comprising:
  20070073538 March 29, 2007 Rifkin
  20100027820 February 4, 2010 Kates
  20170148431 May 25, 2017 Catanzaro
  20170330586 November 16, 2017 Roblek
  20180075343 March 15, 2018 van den Oord
  20180121788 May 3, 2018 Hashimoto
  Patent number: 10878837
  Inventors: Jinxi Guo (Los Angeles, CA), Jia Li (Marina Del Rey, CA), Ning Xu (Irvine, CA)
  Application Number: 15/908,412
  Current U.S. Class: Specialized Equations Or Comparisons (704/236)
  International Classification: G10L 21/00 (20130101); G10L 25/30 (20130101); G06K 9/00 (20060101); G06N 3/04 (20060101); H04S 7/00 (20060101); G06K 9/62 (20060101);