Patent Publication Number: US-11664044-B2

Title: Sound event detection learning

Description:
I. CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application claims priority from Provisional Patent Application No. 62/939,801, filed Nov. 25, 2019, entitled “SOUND EVENT DETECTION LEARNING,” the content of which is incorporated herein by reference in its entirety. 
    
    
     II. FIELD 
     The present disclosure is generally related to sound event detection and to updating sound event detection models. 
     III. DESCRIPTION OF RELATED ART 
     Advances in technology have resulted in smaller and more powerful computing devices. For example, there currently exist a variety of portable personal computing devices, including wireless telephones such as mobile and smart phones, tablets and laptop computers that are small, lightweight, and easily carried by users. These devices can communicate voice and data packets over wireless networks. Further, many such devices incorporate additional functionality such as a digital still camera, a digital video camera, a digital recorder, and an audio file player. Also, such devices can process executable instructions, including software applications, such as a web browser application, that can be used to access the Internet. As such, these devices can include significant computing capabilities. 
     Sound Event Detection (SED) is a research area that has seen recent advances. SED attempts to recognize sound events (e.g., slamming doors, car horns, etc.) in an audio signal. An SED system is generally trained using a supervised machine learning technique to recognize a specific set of sounds that are identified in labeled training data. As a result, each SED system tends to be domain specific (e.g., capable of classifying a predetermined set of sounds). After an SED system is trained, it is difficult to update the SED system to recognize new sounds that were not identified in the labeled training data. For example, an SED system can be trained using a set of labeled audio data samples that include a selection of city noises, such as car horns, sirens, slamming doors, and engine sounds. In this example, if a need arises to also recognize a sound that was not labeled in the set of labeled audio data samples, such as a doorbell, updating the SED system to recognize the doorbell involves completely retraining the SED system using both labeled audio data samples for the doorbell as well as the original set of labeled audio data samples. As a result, training an SED system to recognize a new sound requires approximately the same computing resources (e.g., processor cycles, memory, etc.) as generating a brand-new SED system. Further, over time, as even more sounds are added to be recognized, the number of audio data samples that must be maintained and used to train the SED system can become unwieldy. 
     IV. SUMMARY 
     In a particular aspect, a device includes a processor configured to receive audio data samples. The processor is further configured to provide the audio data samples to a first neural network trained to generate a first output corresponding to a first count of classes of a first set of sound classes. The processor is also configured to provide the audio data samples to a second neural network to generate a second output corresponding to a second count of classes of a second set of sound classes. The second count of classes is greater than the first count of classes. The processor is further configured to provide the first output to a neural adapter to generate a third output corresponding to the second count of classes of the second set of sound classes and to provide the second output and the third output to a merger adapter to generate sound event identification data based on the audio data samples. 
     In a particular aspect, a method includes receiving audio data samples and providing the audio data samples to a first neural network trained to generate a first output corresponding to a first count of classes of a first set of sound classes. The method further includes providing the audio data samples to a second neural network to generate a second output corresponding to a second count of classes of a second set of sound classes. The second count of classes is greater than the first count of classes. The method also includes providing the first output to a neural adapter to generate a third output corresponding to the second count of classes of the second set of sound classes. The method further includes providing the second output and the third output to a merger adapter to generate sound event identification data based on the audio data samples. 
     In a particular aspect, a non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a processor, cause the processor to provide audio data samples to a first neural network trained to generate a first output corresponding to a first count of classes of a first set of sound classes. The instructions further cause the processor to provide the audio data samples to a second neural network to generate a second output corresponding to a second count of classes of a second set of sound classes. The second count of classes is greater than the first count of classes. The instructions also cause the processor to provide the first output to a neural adapter to generate a third output corresponding to the second count of classes of the second set of sound classes. The instructions further cause the processor to provide the second output and the third output to a merger adapter to generate sound event identification data based on the audio data samples. 
     In a particular aspect, a device includes means for generating a first output based on audio data samples, the first output corresponding to a first count of classes of a first set of sound classes. The device further includes means for generating a second output based on the audio data samples, the second output corresponding to a second count of classes of a second set of sound classes. The second count of classes is greater than the first count of classes. The device also includes means for generating a third output based on the first output, the third output corresponding to the second count of classes of the second set of sound classes. The device further includes means for generating sound event identification data based on the third output and the second output. 
     In a particular aspect, a device includes a memory and a processor coupled to the memory. The memory stores a sound event classifier trained to generate first sound identification data in response to input of audio data representing one or more of a first set of sound classes. The processor is configured to generate an updated sound event classifier that is trained to generate second sound identification data in response to input of audio data representing one or more of a second set of sound classes. The second set of count classes includes the first set of sound classes and one or more additional sound classes. The updated sound event classifier includes the sound event classifier, a second sound event classifier, a neural adapter, and a merger adapter. The neural adapter includes one or more adapter layers configured to receive an input having a count of data elements corresponding to an output layer of the sound event classifier and configured to generate an output having a second count of data elements corresponding to an output layer of the second sound event classifier. The merger adapter includes one or more aggregation layers and an output layer. The one or more aggregation layers are configured to merge the output from neural adapter and an output of the second neural network. The output layer is configured to generate the second sound identification data. 
     In a particular aspect, a device includes means for storing a sound event classifier trained to generate first sound identification data in response to input of audio data representing one or more of a first set of sound classes. The device also includes means for generating an updated sound event classifier trained to generate second sound identification data in response to input of audio data representing one or more of a second set of sound classes. The second set of sound classes includes the first set of sound classes and one or more additional sound classes. The updated sound event classifier includes the sound event classifier, a second sound event classifier, a neural adapter, and a merger adapter. The neural adapter includes one or more adapter layers configured to receive an input having a count of data elements corresponding to an output layer of the sound event classifier and configured to generate an output having a second count of data elements corresponding to an output layer of the second sound event classifier. The merger adapter includes one or more aggregation layers and an output layer. The one or more aggregation layers are configured to merge the output from the neural adapter and an output of the second sound event classifier. The output layer is configured to generate the second sound identification data. 
     In a particular aspect, a method includes generating a second neural network based on a first neural network of a first sound event classifier. The first neural network includes an input layer, hidden layers, and a first output layer, and the second neural network includes a copy of the input layer of the first neural network, a copy of the hidden layers of the first neural network, and a second output layer coupled to the copy of the hidden layers. The first output layer includes a first count of output nodes and the second output layer includes a second count of output node, where the second count of output nodes is greater than the first count of output nodes. The method also includes linking the first neural network and the second neural network to one or more adapter networks and providing labeled training data as input to the first neural network and to the second neural network. The method also includes modifying output of the first neural network and the second neural network via the one or more adapter networks. The method further includes training a second sound event classifier by updating link weights of the second neural network and of the one or more adapter networks based on output of the adapter networks and labels of the labeled training data. 
     In a particular aspect, a non-transitory computer-readable storage medium includes instructions that when executed by a processor, cause the processor to generate a second neural network based on a first neural network of a first sound event classifier. The first neural network includes an input layer, hidden layers, and a first output layer, and the second neural network includes a copy of the input layer of the first neural network, a copy of the hidden layers of the first neural network, and a second output layer coupled to the copy of the hidden layers. The first output layer includes a first count of output nodes and the second output layer includes a second count of output node, where the second count of output nodes is greater than the first count of output nodes. The instructions further cause the processor to link the first neural network and the second neural network to one or more adapter networks. The instructions also cause the processor to provide labeled training data as input to the first neural network and to the second neural network and modify output of the first neural network and the second neural network via the one or more adapter networks. The instructions further cause the processor to train a second sound event classifier by updating link weights of the second neural network and of the one or more adapter networks based on output of the adapter networks and labels of the labeled training data. 
     Other aspects, advantages, and features of the present disclosure will become apparent after review of the entire application, including the following sections: Brief Description of the Drawings, Detailed Description, and the Claims. 
    
    
     
       V. BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a block diagram of an example of a device that is configured to generate sound identification data responsive to audio data samples and configured to generate an updated sound event detection model. 
         FIG.  2    a block diagram illustrating aspects of a sound event detection model according to a particular example. 
         FIG.  3    is a diagram that illustrates aspects of generating an updated sound event detection model according to a particular example. 
         FIG.  4    is a diagram that illustrates aspects of generating sound event detection output data using an updated sound event detection model according to a particular example 
         FIG.  5    is an illustrative example of a vehicle that incorporates aspects of the updated sound event detection model of  FIG.  1   ,  FIG.  3   , or  FIG.  4   . 
         FIG.  6    illustrates virtual reality or augmented reality headset that incorporates aspects of the updated sound event detection model of  FIG.  1   ,  FIG.  3   , or  FIG.  4   . 
         FIG.  7    illustrates a wearable electronic device that incorporates aspects of the updated sound event detection model of  FIG.  1   ,  FIG.  3   , or  FIG.  4   . 
         FIG.  8    illustrates a voice-controlled speaker system that incorporates aspects of the updated sound event detection model of  FIG.  1   ,  FIG.  3   , or  FIG.  4   . 
         FIG.  9    illustrates a camera that incorporates aspects of the updated sound event detection model of  FIG.  1   ,  FIG.  3   , or  FIG.  4   . 
         FIG.  10    illustrates a mobile device that incorporates aspects of the updated sound event detection model of  FIG.  1   ,  FIG.  3   , or  FIG.  4   . 
         FIG.  11    illustrates an aerial device that incorporates aspects of the updated sound event detection model of  FIG.  1   ,  FIG.  3   , or  FIG.  4   . 
         FIG.  12    illustrates a headset that incorporates aspects of the updated sound event detection model of  FIG.  1   ,  FIG.  3   , or  FIG.  4   . 
         FIG.  13    illustrates an appliance that incorporates aspects of the updated sound event detection model of  FIG.  1   ,  FIG.  3   , or  FIG.  4   . 
         FIG.  14    is a flow chart illustrating an example of a method of generating sound event detection data using the updated sound event detection model of  FIG.  1   ,  FIG.  3   , or  FIG.  4   . 
         FIG.  15    is a flow chart illustrating an example of a method of generating the updated sound event detection model of  FIG.  1   ,  FIG.  3   , or  FIG.  4   . 
     
    
    
     VI. DETAILED DESCRIPTION 
     Sound event detection models can be trained using machine-learning techniques. For example, a neural network can be trained as a sound event classifier using backpropagation or other machine-learning training techniques. A sound event detection model trained in this manner can be small enough (in terms of storage space occupied) and simple enough (in terms of computing resources used during operation) for a portable computing device to store and use the sound event detection model. However, the training process uses significantly more processing resources than are used to perform sound event detection using the trained sound event detection model. Additionally, the training process uses a large set of labeled training data including many audio data samples for each sound class that the sound event detection model is being trained to detect. Thus, it may be prohibitive in terms of memory utilization or other computing resources, to train a sound event detection model from scratch on a portable computing device or another resource limited computing device. As a result, a user who desires to use a sound event detection model on a portable computing device may be limited to downloading pre-trained sound event detection models onto the portable computing device from a less resource constrained computing device or a library of pre-trained sound event detection models. Thus, the user has limited customization options. 
     The disclosed systems and methods use transfer learning techniques to generate updated sound event detection models in a manner that is significantly less resource intensive than training sound event detection models from scratch. According to a particular aspect, the transfer learning techniques can be used to generate an updated sound event detection model based on a previously trained sound event detection model (also referred to herein as a “base model”). The updated sound event detection model is configured to detect more types of sound events than the base model is. For example, the base model is trained to detect any of a first set of sound events, each of which corresponds to a sound class of a first set of sound classes, and the updated sound event detection model is trained to detect any of the first set of sound events as well as any of a second set of sound events, each of which corresponds to a sound class of a second set of sound classes. Accordingly, the disclosed systems and methods reduce the computing resources (e.g., memory, processor cycles, etc.) used to generate an updated sound event detection model. As one example of a use case for the disclosed system and methods, a portable computing device can be used to generate a custom sound event detector. 
     According to a particular aspect, an updated sound event detection model is generated based on a previously trained sound event detection model, a subset of the training data used to train the previously trained sound event detection model, and one or more sets of training data corresponding to one or more additional sound classes that the updated sound event detection model is to be able to detect. In this aspect, the previously trained sound event detection model (e.g., a first model) is retained and unchanged. Additionally, a copy of the previously trained sound event detection model (e.g., a second model) is generated and modified to have a new output layer. The new output layer includes an output node for each sound class that the updated sound event detection model is to be able to detect. For example, if the first model is configured to detect ten distinct sound classes, then an output layer of the first model may include ten output nodes. In this example, if the updated sound event detection model is to be trained to detect twelve distinct sound classes (e.g., the ten sound classes that the first model is configured to detect plus two additional sound classes), then the output layer of the second model includes twelve output nodes. 
     One or more adapter networks are generated to link output of the first model and output of the second model. For example, the adapter network(s) convert an output of the first model to have a size corresponding to an output of the second model. To illustrate, in the example of the previous paragraph, the first model includes ten output nodes and generates an output having ten data elements, and the second model includes twelve output nodes and generates an output having twelve data elements. In this example, the adapter network(s) modify the output of the first model to have twelve data elements. The adapter network(s) also combine the output of the second model and the modified output of the first model to generate a sound classification output of the updated sound event detection model. 
     The updated sound event detection model is trained using labeled training data that includes audio data samples and labels for each sound class that the updated sound event detection model is being trained to detect. However, since the first model is already trained to accurately detect the first set of sound classes, the labeled training data includes far fewer audio data samples for the first set of sound classes than were originally used to train the first model. To illustrate, the first model can be trained using hundreds or thousands of audio data samples for each sound class of the first set of sound classes. In contrast, the labeled training data used to train the updated sound event detection model can include tens or fewer of audio data samples for each sound class of the first set of sound classes. The labeled training data also includes audio data samples for each sound class of the second set of sound classes. The audio data samples for the second set of sound classes can also include tens or fewer audio data samples for each sound class of the second set of sound classes. 
     Backpropagation or another machine-learning technique is used to train the second model and the one or more adapter networks. During this process, the first model is unchanged, which limits or eliminates the risk that the first model will forget its prior training. For example, during its previous training, the first model was trained using a large labeled training data set to accurately detect the first set of sound classes. Retraining the first model using the relatively small labeled training data set used during backpropagation risks causing the accuracy of the first model to decline (sometimes referred to as “forgetting” some of its prior training). Retaining the first model unchanged while training the updated sound event detector model mitigates the risk of forgetting the first set of sound classes. 
     Additionally, before training, the second model is identical to the first model except for the output layer of the second model and interconnections therewith. Thus, at the starting point of the training, the second model is expected to be closer to convergence (e.g., closer to a training termination condition) than a randomly seeded model. As a result, fewer iterations should be needed to train the second model than were used to train the first model. 
     After the updated sound event detection model is trained, the updated sound event detection model (rather than the first model alone) can be used to detect sound events. For example, when audio data samples are received, the audio data samples are provided as input to the updated sound event detection model, and the updated sound event detection model generates a sound classification output. Within the updated sound event detection model, the audio data samples are provided as input to the first model and to the second model. The first model generates a first output, which is provided to the adapter network(s), modified, and combined with a second output from the second model. The adapter network(s) generate a third output corresponding to the sound classification output of the updated sound event detection model. 
     Particular aspects of the present disclosure are described below with reference to the drawings. In the description, common features are designated by common reference numbers. As used herein, various terminology is used for the purpose of describing particular implementations only and is not intended to be limiting of implementations. For example, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. Further, some features described herein are singular in some implementations and plural in other implementations. To illustrate,  FIG.  1    depicts a device  100  including one or more microphone (“microphone(s)  114  in  FIG.  1   ), which indicates that in some implementations the device  100  includes a single microphone  114  and in other implementations the device  100  includes multiple microphones  114 . For ease of reference herein, such features are generally introduced as “one or more” features and are subsequently referred to in the singular or optional plural (generally indicated by terms ending in “(s)”) unless aspects related to multiple of the features are being described. 
     It may be further understood that the terms “comprise,” “comprises,” and “comprising” may be used interchangeably with “include,” “includes,” or “including.” Additionally, it will be understood that the term “wherein” may be used interchangeably with “where.” As used herein, “exemplary” may indicate an example, an implementation, and/or an aspect, and should not be construed as limiting or as indicating a preference or a preferred implementation. As used herein, an ordinal term (e.g., “first,” “second,” “third,” etc.) used to modify an element, such as a structure, a component, an operation, etc., does not by itself indicate any priority or order of the element with respect to another element, but rather merely distinguishes the element from another element having a same name (but for use of the ordinal term). As used herein, the term “set” refers to one or more of a particular element, and the term “plurality” refers to multiple (e.g., two or more) of a particular element. 
     As used herein, “coupled” may include “communicatively coupled,” “electrically coupled,” or “physically coupled,” and may also (or alternatively) include any combinations thereof. Two devices (or components) may be coupled (e.g., communicatively coupled, electrically coupled, or physically coupled) directly or indirectly via one or more other devices, components, wires, buses, networks (e.g., a wired network, a wireless network, or a combination thereof), etc. Two devices (or components) that are electrically coupled may be included in the same device or in different devices and may be connected via electronics, one or more connectors, or inductive coupling, as illustrative, non-limiting examples. In some implementations, two devices (or components) that are communicatively coupled, such as in electrical communication, may send and receive electrical signals (digital signals or analog signals) directly or indirectly, such as via one or more wires, buses, networks, etc. As used herein, “directly coupled” may include two devices that are coupled (e.g., communicatively coupled, electrically coupled, or physically coupled) without intervening components. 
     In the present disclosure, terms such as “determining,” “calculating,” “estimating,” “shifting,” “adjusting,” etc. may be used to describe how one or more operations are performed. It should be noted that such terms are not to be construed as limiting and other techniques may be utilized to perform similar operations. Additionally, as referred to herein, “generating,” “calculating,” “estimating,” “using,” “selecting,” “accessing,” and “determining” may be used interchangeably. For example, “generating,” “calculating,” “estimating,” or “determining” a parameter (or a signal) may refer to actively generating, estimating, calculating, or determining the parameter (or the signal) or may refer to using, selecting, or accessing the parameter (or signal) that is already generated, such as by another component or device. 
       FIG.  1    is a block diagram of an example of a device  100  that is configured to generate sound identification data responsive to input of audio data samples. In  FIG.  1   , the device  100  is also configured to generate an updated sound event detection model. In some implementations, a remote computing device  150  generates the updated sound event detection model, and the device  100  uses the updated sound event detection model to generate sound identification data responsive to audio data samples. In some implementations, the remote computing device  150  and the device  100  cooperate to generate the updated sound event detection model, and the device  100  uses the updated sound event detection model to generate sound identification data responsive to audio data samples. In various implementations, the device  100  may have more or fewer components than illustrated in  FIG.  1   . 
     In a particular implementation, the device  100  includes a processor  120  (e.g., a central processing unit (CPU)). The device  100  may include one or more additional processor(s)  132  (e.g., one or more DSPs). The processor  120 , the processor(s)  132 , or both, may be configured to generate sound identification data, to generate an updated sound event detection model, or both. For example, in  FIG.  1   , the processor(s)  132  include a sound event detection (SED) engine  108 . The SED engine  108  is configured to analyze audio data samples using a sound event classifier, such as a base model  104  or an update model  106 . The base model  104  is a previously trained sound event detection model. In some implementations, another computing device, such as the remote computing device  150 , trains the base model  104  and the base model  104  is stored on the device  100  as a default model, or the device downloads the base model from the other computing device. In some implementations, the device  100  trains the base model  104 . Training the base model  104  entails use of a relatively large set of labeled training data (e.g., base training data  152  in  FIG.  1   ). In some implementations whether the remote computing device  150  or the device  100  trains the base model  104 , the base training data  152  is stored at the remote computing device  150 , which may have greater storage capacity (e.g., more memory) than the device  100 .  FIG.  2    illustrates examples of particular implementations of the base model  104 . The update model  106  is an updated sound event detection model that is based on the base model  104  and trained, as described further below, using a model updater  110 . 
     In  FIG.  1   , the device  100  also includes a memory  130  and a CODEC  142 . The memory  130  stores instructions  124  that are executable by the processor  120 , or the processor(s)  132 , to implement one or more operations described with reference to  FIGS.  3 - 15   . In an example, the instructions  124  include or correspond to the SED engine  108 , the model updater  110 , or both. The memory  130  may also store the base model  104 , the update model  106 , or both. Further, in the example illustrated in  FIG.  1   , the memory  130  stores audio data samples  126  and audio data samples  128 . The audio data samples  126  include audio data samples representing one or more of a first set of sound classes used to train the base model  104 . That is, the audio data samples  126  include a relatively small subset of the base training data  152 . In some implementations, the device  100  downloads the audio data samples  126  from the remote computing device  150  when the device  100  is preparing to generate the update model  106 . The audio data samples  128  include audio data samples representing one or more of a second set of sound classes used to train the update model  106 . In a particular implementation, the device  100  captures one or more of the audio data samples  128  (e.g., using the microphone(s)  114 ). In some implementations, the device  100  obtains one or more of the audio data samples  128  from another device, such as the remote computing device  150 .  FIG.  3    illustrates an example of operation of the model updater  110  to generate the update model  106  based on the base model  104 , the audio data samples  126 , and the audio data samples  128 . 
     In  FIG.  1   , speaker(s)  118  and the microphone(s)  114  may be coupled to the CODEC  142 . In a particular aspect, the microphone(s)  114  are configured to receive audio representing an acoustic environment associated with the device  100  and to generate audio data samples that the SED engine  108  provides to the base model  104  or to the update model  106  to generate a sound classification output.  FIG.  4    illustrates an example of operation of the update model  106  to generate output data indicating detection of a sound event. The microphone(s)  114  may also be configured to provide the audio data samples  128  to the model updater  110  or to the memory  130  for use in generating the update model  106 . 
     In the example illustrated in  FIG.  1   , the CODEC  142  includes a digital-to-analog converter (DAC  138 ) and an analog-to-digital converter (ADC  140 ). In a particular implementation, the CODEC  142  receives analog signals from the microphone(s)  114 , converts the analog signals to digital signals using the ADC  140 , and provides the digital signals to the processor(s)  132 . In a particular implementation, the processor(s)  132  (e.g., the speech and music codec) provide digital signals to the CODEC  142 , and the CODEC  142  converts the digital signals to analog signals using the DAC  138  and provides the analog signals to the speaker(s)  118 . 
     In  FIG.  1   , the device  100  also includes an input device  122 . The device  100  may also include a display  102  coupled to a display controller  112 . In a particular aspect, the input device  122  includes a sensor, a keyboard, a pointing device, etc. In some implementations, the input device  122  and the display  102  are combined in a touchscreen or similar touch or motion sensitive display. The input device  122  can be used to provide a label associated with one of the audio data samples  128  to generate labeled training data used to train the update model  106 . In some implementations, the device  100  also includes a modem  136  coupled a transceiver  134 . In  FIG.  1   , the transceiver  134  is coupled to an antenna  146  to enable wireless communication with other devices, such as the remote computing device  150 . In other examples, the transceiver  134  is also, or alternatively, coupled to a communication port (e.g., an ethernet port) to enable wired communication with other devices, such as the remote computing device  150 . 
     In a particular implementation, the device  100  is included in a system-in-package or system-on-chip device  144 . In a particular implementation, the memory  130 , the processor  120 , the processor(s)  132 , the display controller  112 , the CODEC  142 , the modem  136 , and the transceiver  134  are included in a system-in-package or system-on-chip device  144 . In a particular implementation, the input device  122  and a power supply  116  are coupled to the system-on-chip device  144 . Moreover, in a particular implementation, as illustrated in  FIG.  1   , the display  102 , the input device  122 , the speaker(s)  118 , the microphone(s)  114 , the antenna  146 , and the power supply  116  are external to the system-on-chip device  144 . In a particular implementation, each of the display  102 , the input device  122 , the speaker(s)  118 , the microphone(s)  114 , the antenna  146 , and the power supply  116  may be coupled to a component of the system-on-chip device  144 , such as an interface or a controller. 
     The device  100  may include, correspond to, or be included within a voice activated device, an audio device, a wireless speaker and voice activated device, a portable electronic device, a car, a vehicle, a computing device, a communication device, an internet-of-things (IoT) device, a virtual reality (VR) device, an augmented reality (AR) device, a smart speaker, a mobile computing device, a mobile communication device, a smart phone, a cellular phone, a laptop computer, a computer, a tablet, a personal digital assistant, a display device, a television, a gaming console, an appliance, a music player, a radio, a digital video player, a digital video disc (DVD) player, a tuner, a camera, a navigation device, or any combination thereof. In a particular aspect, the processor  120 , the processor(s)  132 , or a combination thereof, are included in an integrated circuit. 
       FIG.  2    is a block diagram illustrating aspects of the base model  104  according to a particular example. The base model  104  is a neural network that has a topology (e.g., a base topology  202 ) and trainable parameters (e.g., base parameters  236 ). The base topology  202  can be represented as a set of nodes and edges (or links); however, for ease of illustration and reference, the base topology  202  is represented in  FIG.  2    as a set of layers. It should be understood that each layer of  FIG.  2    includes a set of nodes, and that links interconnect the nodes of the different layers. The arrangement of the links depends on the type of each layer. 
     During backpropagation training, the base topology  202  is static and the base parameters  236  are changed. In  FIG.  2   , the base parameters  236  include base link weights  238 . The base parameters  236  may also include other parameters, such as a bias value associated with one or more nodes of the base model  104 . 
     The base topology  202  includes an input layer  204 , one or more hidden layers (labeled hidden layer(s)  206  in  FIG.  2   ), and an output layer  234 . A count of input nodes of the input layer  204  depends on the arrangement of the audio data samples to be provided to the base model  104 . For example, the audio data samples may include an array or matrix of data elements, with each data element corresponding to a feature of an input audio sample. As a specific example, the audio data samples can correspond to Mel spectrum features extracted from one second of audio data. In this example, the audio data samples can include a 128×128 element matrix of feature values. In other examples, other audio data sample configurations or sizes can be used. A count of nodes of the output layer  234  depends on a number of sound classes that the base model  104  is configured to detect. As an example, the output layer  234  may include one output node for each sound class. 
     The hidden layer(s)  206  can have various configurations and various numbers of layers depending on the specific implementations.  FIG.  2    illustrates one particular example of the hidden layer(s)  206 . In  FIG.  2   , the hidden layer(s)  206  include three convolutional neural networks (CNNs), including a CNN  208 , a CNN  228 , and a CNN  230 . In this example, the output layer  234  includes or corresponds to an activation layer  232 . For example, the activation layer  232  receives the output of the CNN  230  and applies an activation function (such as a sigmoid function) to the output to generate as output a set of data elements which each include either a one value or a zero value. 
       FIG.  2    also illustrates details of one particular implementation of the CNN  208 , the CNN  228 , and the CNN  230 . In the specific example illustrated in  FIG.  2   , the CNN  208  includes a two-dimensional (2D) convolution layer (conv2d  210  in  FIG.  2   ), a maxpooling layer (maxpool  216  in  FIG.  2   ), and batch normalization layer (batch norm  226  in  FIG.  2   ). Likewise, in  FIG.  2   , the CNN  228  includes a conv2d  212 , a maxpool  222 , and a batch norm  220 , and the CNN  230  includes a conv2d  214 , a maxpool  224 , and a batch norm  218 . In other implementations, the hidden layer(s)  206  include a different number of CNNs or other layers. 
     As explained above, the update model  106  includes the base model  104 , a modified copy of the base model  104 , and one or more adapter networks. The modified copy of the base model  104  uses the same base topology  202  as illustrated in  FIG.  2    except that an output layer of the modified copy includes more output nodes than the output layer  234 . Additionally, before training the update model  106 , the modified copy is initialized to have the same base parameters  236  as the base model  104 . 
       FIG.  3    is a diagram that illustrates aspects of generating the update model  106  according to a particular example. The operations described with reference to  FIG.  3    can be initiated, performed, or controlled by the processor  120  or the processor(s)  132  of  FIG.  1    executing the instructions  124 . Alternatively, the operations described with reference to  FIG.  3    may be performed by the remote computing device  150  using audio data samples  128  captured at the device  100  and audio data samples  126  from the base training data  152 . If the operations described with reference to  FIG.  3    are performed at the remote computing device  150 , the device  100  may download the update model  106  or a portion thereof, such as an incremental model  302  and one or more adapter networks  314 , from the remote computing device  150 . 
     To generate the update model  106 , the model updater  110  copies the base model  104  and replaces the output layer  234  of the copy of the base model  104  with a different output layer (e.g., an output layer  322  in  FIG.  3   ) to generate an incremental model  302  (also referred to herein as a second model). The incremental model  302  includes the base topology  202  of the base model  104  except for replacement of the output layer  234  with the output layer  322  and links generated to link the output nodes of the output layer  322  to hidden layers of the incremental model  302 . Model parameters of the incremental model  302  (e.g., incremental model parameters  306 ) are initialized to be equal to the base parameters  236 . The output layer  234  of the base model  104  includes a first count of nodes (e.g., N nodes in  FIG.  3   , where N is a positive integer), and the output layer  322  of the incremental model  302  includes a second count of nodes (e.g., N+K nodes in  FIG.  3   , where K is a positive integer). The first count of nodes corresponds to the count of sound classes of a first set of sound classes that the base model  104  is trained to recognize (e.g., the first set of sound classes includes N distinct sound classes that the base model  104  can recognize). The second count of nodes corresponds to the count of sound classes of a second set of sound classes that the update model  106  is to be trained to recognize (e.g., the second set of sound classes includes N+K distinct sound classes that the update model  106  is to be trained to recognize). Thus, the second set of sound classes includes the first set of sound classes (e.g., N classes) plus one or more additional sound classes (e.g., K classes). 
     In addition to generating the incremental model  302 , the model updater  110  generates one or more adapter network(s)  314 . In  FIG.  3   , the adapter network(s)  314  include a neural adapter  310  and a merger adapter  308 . The neural adapter  310  includes one or more adapter layers (e.g., adapter layer(s)  312  in  FIG.  3   ). The adapter layer(s)  312  are configured to receive input from the base model  104  and to generate output that can be merged with the output of the incremental model  302 . For example, the base model  104  can generate as output a first output  352  corresponding to the first count of classes of the first set of sound classes. For example, the first output include one data element for each node of the output layer  234  (e.g., N data elements). In contrast, the incremental model  302  generates as output a second output  354  corresponding to the second count of classes of the second set of sound classes. For example, the second output  354  includes one data element for each node of the output layer  322  (e.g., N+K data elements). In this example, the adapter layer(s)  312  receive an input having the first count of data elements and generate a third output  356  having the second count of data elements (e.g., N+K). In a particular example, the adapter layer(s)  312  include two fully connected layers (e.g., an input layer including N nodes and an output layer including N+K nodes, with each node of the input layer connected to every node of the output layer). 
     The merger adapter  308  is configured to generate output data  318  by merging the third output  356  from the neural adapter  310  and the second output  354  of the incremental model  302 . In  FIG.  3   , the merger adapter  308  includes an aggregation layer  316  and an output layer  320 . The aggregation layer  316  is configured to combine the second output  354  and the third output  356  in an element-by-element manner. For example, the aggregation layer  316  can add each element of the third output  356  from the neural adapter  310  to a corresponding element of the second output  354  from the incremental model  302  and provide the resulting merged output to the output layer  320 . The output layer  320  is an activation layer that applies an activation function (such as a sigmoid function) to the merged output to generate the output data  318 . The output data  318  includes or corresponds to a sound event identifier  360  indicating a sound class to which the update model  106  assigns a particular audio sample (e.g., one of the audio data samples  126  or  128 ). 
     During training, the model updater  110  provides labeled training data  304  to the base model  104  and the incremental model  302 . The labeled training data  304  includes one or more of the audio data samples  126  (which were used to train the base model  104 ) and one or more audio data samples  128  (which correspond to new sound classes that the base model  104  is not trained to recognize). In response to a particular audio sample of the labeled training data  304 , the base model  104  generates the first output  352  that is provided as input to the neural adapter  310 . Additionally, in response to the particular audio sample, the incremental model  302  generates the second output  354  that is provided, along with the third output  356  of the neural adapter  310 , to the merger adapter  308 . The merger adapter  308  merges the second output  354  and third output  356  to generate a merged output and generates the output data  318  based on the merged output. 
     The output data  318 , the sound event identifier  360 , or both, are provided to the model updater  110  which compares the sound event identifier  360  to a label associated, in the labeled training data  304 , with the particular audio sample and calculates updated link weight values (updated link weights  362  in  FIG.  3   ) to modify the incremental model parameters  306 , link weights of the neural adapter  310 , link weights of the merger adapter  308 , or a combination thereof. The training process continues iteratively until the model updater  110  determines that a training termination condition is satisfied. For example, the model updater  110  calculates an error value based on the labeled training data  304  and the output data  318 . In this example, the error value indicates how accurately the update model  106  classifies each audio data sample  126  and  128  of the labeled training data  304  based on a label associated with each audio data sample  126  and  128 . In this example, the training termination condition may be satisfied when error value is less than a threshold or when a change in the error value between training iterations is less than a threshold. In some implementations, the termination condition is satisfied when a count of training iterations performed is greater than or equal to a threshold count. 
       FIG.  4    is a diagram that illustrates aspects of using the update model  106  to generate sound event detection output data according to a particular example. The operations described with reference to  FIG.  4    can be initiated, performed, or controlled by the processor  120  or the processor(s)  132  of  FIG.  1    executing the instructions  124 . 
     In  FIG.  4   , one or more inputs  450  including audio data samples  406  are provided to the base model  104  and to the incremental model  302  of the update model  106 . In a particular example, the audio data samples  406  includes, corresponds to, or are based on audio captured by the microphone(s)  114  of the device  100  of  FIG.  1   . For example, the audio data samples  406  may correspond to features extracted from several seconds of audio data, and the input  450  may include an array or matrix of feature data extracted from the audio data. 
     In response to the input  450 , the base model  104  generates a first output  452  that is provided as input to the neural adapter  310 . The base model  104  generates the first output  452  using the base parameters  236 , including the base link weights  238 . The first output  452  of the base model  104  corresponds to the first count of classes of the first set of sound classes. In an illustrative example, the first output  452  includes an array or matrix of data elements and has a count of data element (e.g., N data elements) corresponding to the number of output nodes (e.g., N nodes) of the output layer  234  of the base model  104 , and the number of output nodes of the output layer  234  corresponds to the first count of classes of the first set of sound classes. 
     In response to the input  450 , the incremental model  302  generates a second output  454  that is provided to the merger adapter  308 . The second output  454  of the incremental model  302  corresponds to the second count of classes of the second set of sound class. In an illustrative example, the second output  454  includes an array or matric of data elements and has a count of data element (e.g., N+K data elements) corresponding to the number of output nodes (e.g., N+K nodes) of the output layer  322  of the incremental model  302 , and the number of output nodes of the output layer  322  corresponds to the second count of classes of the second set of sound classes. The incremental model  302  generates the second output  454  using updated parameters  402 , including updated link weights  404 , which are generated by the model updater  110  during the training process. In a particular implementation, the updated parameters  402  correspond to the parameters of the incremental model  302  that satisfied the training termination condition described with reference to  FIG.  3   . 
     The neural adapter  310  generates a third output  456  based on the first output  452  from the base model  104 . In a particular example, the neural adapter  310  generates the third output  456  based on link weights trained by the model updater  110  during the training process. The third output  456  of the neural adapter  310  corresponds to the second count of classes of the second set of sound class. In an illustrative example, the third output  456  includes an array or matrix of data elements and has a count of data element (e.g., N+K data elements) corresponding to the second count of classes of the second set of sound classes. 
     The third output  456  from the neural adapter  310  and the second output  454  from the incremental model  302  are provided to the merger adapter  308 . The aggregation layer  316  of the merger adapter  308  combines the third output  456  and the second output  454 , element-by-element, to generate a merged output  458 . The output layer  320  of the merger adapter  308  generates the output data  408  based on the merged output  458 . In a particular example, the merger adapter  308  generates the output data  408  based on link weights trained by the model updater  110  during the training process. The output data  408  includes sound identification data (e.g., a sound event identification data  460 ) indicating a sound class associated with the audio data samples  406 . 
       FIG.  5    is an illustrative example of a vehicle  500  that incorporates aspects of the updated sound event detection model  106  of  FIG.  1   ,  FIG.  3   , or  FIG.  4   . According to one implementation, the vehicle  500  is a self-driving car. According to other implementations, the vehicle  500  is a car, a truck, a motorcycle, an aircraft, a water vehicle, etc. In  FIG.  5   , the vehicle  500  includes a screen  502  (e.g., a display, such as the display  102  of  FIG.  1   ), sensor(s)  504 , the device  100 , or a combination thereof. The sensor(s)  504  and the device  100  are shown using a dotted line to indicate that these components might not be visible to passengers of the vehicle  500 . The device  100  can be integrated into the vehicle  500  or coupled to the vehicle  500 . 
     In a particular aspect, the device  100  is coupled to the screen  502  and provides an output to the screen  502  responsive to detecting or recognizing various events (e.g., sound events) described herein. For example, the device  100  provides the output data  408  of  FIG.  4    to the screen  502  indicating that a recognized sound event, such as a car horn, is detected in audio data received from the sensor(s)  504 . In some implementations, the device  100  can perform an action responsive to recognizing a sound event, such as activating a camera or one of the sensor(s)  504 . In a particular example, the device  100  provides an output that indicates whether an action is being performed responsive to the recognized sound event. In a particular aspect, a user can select an option displayed on the screen  502  to enable or disable a performance of actions responsive to recognized sound events. 
     In a particular implementations, the sensor(s)  504  include one or more microphone(s)  114  of  FIG.  1   , vehicle occupancy sensors, eye tracking sensor, or external environment sensors (e.g., lidar sensors or cameras). In a particular aspect, sensor input of the sensor(s)  504  indicates a location of the user. For example, the sensor(s)  504  are associated with various locations within the vehicle  500 . 
     The device  100  in  FIG.  5    includes the base model  104 , the update model  106 , the SED engine  108 , and the model updater  110 . However, in other implementations, the device  100 , when installed in or used in the vehicle  500 , omits the model updater  110 . To illustrate, the remote computing device  150  of  FIG.  1    may generate the update model  106 . In such implementations, the update model  106  can be downloaded to the vehicle  500  for used by the SED engine  108 . 
     Thus, the techniques described with respect to  FIGS.  1 - 4    enable a user of the vehicle  500  to update a sound event detection model (e.g., the base model  104 ) stored in a memory of the vehicle  500  to generate a sound event detection model (e.g., the update model  106 ) that is able to detect a new set of sound classes. In addition, the sound event detection model can be updated without excessive use of computing resources onboard the vehicle  500 . For example, the vehicle  500  does not have to store all of the base training data  152  used train the base model  104  in a local memory in order to avoid forgetting training associated with the base training data  152 . Rather, the model updater  110  retains the base model  104  while generating the update model  106 . The model update process also converges faster (e.g., uses fewer processor cycles) than would be used to generate a new sound event detection model from scratch. 
       FIG.  6    depicts an example of the device  100  coupled to or integrated within a headset  602 , such as a virtual reality headset, an augmented reality headset, a mixed reality headset, an extended reality headset, a head-mounted display, or a combination thereof. A visual interface device, such as a display  604 , is positioned in front of the user&#39;s eyes to enable display of augmented reality or virtual reality images or scenes to the user while the headset  602  is worn. In a particular example, the display  604  is configured to display output of the device  100 , such as an indication of a recognized sound event (e.g., the sound event identification data  460 ). The headset  602  can include one or more sensor(s)  606 , such as microphone(s)  114  of  FIG.  1   , cameras, other sensors, or a combination thereof. Although illustrated in a single location, in other implementations one or more of the sensor(s)  606  can be positioned at other locations of the headset  602 , such as an array of one or more microphones and one or more cameras distributed around the headset  602  to detect multi-modal inputs. 
     The sensor(s)  606  enable detection of audio data, which the device  100  uses to detect sound events or to update the base model  104  to generate the update model  106 . For example, the device  100  provides the output data  408  of  FIG.  4    to the display  604  indicating that a recognized sound event, such as a car horn, is detected in audio data received from the sensor(s)  606 . In some implementations, the device  100  can perform an action responsive to recognizing a sound event, such as activating a camera or one of the sensor(s)  606  or providing haptic feedback to the user. 
     In the example illustrated in  FIG.  6   , the device  100  includes the base model  104 , the update model  106 , the SED engine  108 , and the model updater  110 . However, in other implementations, the device  100 , when installed in or used in the headset  602 , omits the model updater  110 . To illustrate, the remote computing device  150  of  FIG.  1    may generate the update model  106 . In such implementations, the update model  106  can be downloaded to the headset  602  for used by the SED engine  108 . 
       FIG.  7    depicts an example of the device  100  integrated into a wearable electronic device  702 , illustrated as a “smart watch,” that includes a display  706  (e.g., the display  102  of  FIG.  1   ) and sensor(s)  704 . The sensor(s)  704  enable detection, for example, of user input based on modalities such as video, speech, and gesture. The sensor(s)  704  also enable detection of audio data, which the device  100  uses to detect sound events or to update the base model  104  to generate the update model  106 . For example, the sensor(s)  704  may include or correspond to the microphone(s)  114  of  FIG.  1   . 
     The sensor(s)  704  enable detection of audio data, which the device  100  uses to detect sound events or to update the base model  104  to generate the update model  106 . For example, the device  100  provides the output data  408  of  FIG.  4    to the display  706  indicating that a recognized sound event is detected in audio data received from the sensor(s)  704 . In some implementations, the device  100  can perform an action responsive to recognizing a sound event, such as activating a camera or one of the sensor(s)  704  or providing haptic feedback to the user. 
     In the example illustrated in  FIG.  7   , the device  100  includes the base model  104 , the update model  106 , the SED engine  108 , and the model updater  110 . However, in other implementations, the device  100 , when installed in or used in the wearable electronic device  702 , omits the model updater  110 . To illustrate, the remote computing device  150  of  FIG.  1    may generate the update model  106 . In such implementations, the update model  106  can be downloaded to the wearable electronic device  702  for used by the SED engine  108 . 
       FIG.  8    is an illustrative example of a voice-controlled speaker system  800 . The voice-controlled speaker system  800  can have wireless network connectivity and is configured to execute an assistant operation. In  FIG.  8   , the device  100  is included in the voice-controlled speaker system  800 . The voice-controlled speaker system  800  also includes a speaker  802  and sensor(s)  804 . The sensor(s)  804  can include one or more microphone(s)  114  of  FIG.  1    to receive voice input or other audio input. 
     During operation, in response to receiving a verbal command, the voice-controlled speaker system  800  can execute assistant operations. The assistant operations can include adjusting a temperature, playing music, turning on lights, etc. The sensor(s)  804  enable detection of audio data, which the device  100  uses to detect sound events or to generate the update model  106 . Additionally, the voice-controlled speaker system  800  can execute some operations based on sound events recognized by the device  100 . For example, if the device  100  recognizes the sound of a door closing, the voice-controlled speaker system  800  can turn on one or more lights. 
     In the example illustrated in  FIG.  8   , the device  100  includes the base model  104 , the update model  106 , the SED engine  108 , and the model updater  110 . However, in other implementations, the device  100 , when installed in or used in the voice-controlled speaker system  800 , omits the model updater  110 . To illustrate, the remote computing device  150  of  FIG.  1    may generate the update model  106 . In such implementations, the update model  106  can be downloaded to the voice-controlled speaker system  800  for used by the SED engine  108 . 
       FIG.  9    illustrates a camera  900  that incorporates aspects of the updated sound event detection model of  FIG.  1   ,  FIG.  3   , or  FIG.  4   . In  FIG.  9   , the device  100  is incorporated in or coupled to the camera  900 . The camera  900  includes an image sensor  902  and one or more other sensors  904 , such as the microphone(s) 114  of  FIG.  1   . Additionally, the camera  900  includes the device  100 , which is configured to identify sound events based on audio data from the sensor(s)  904 . For example, the camera  900  may cause the image sensor  902  to capture an image in response to the device  100  detecting a particular sound event in the audio data from the sensor(s)  904 . 
     In the example illustrated in  FIG.  9   , the device  100  includes the base model  104 , the update model  106 , the SED engine  108 , and the model updater  110 . However, in other implementations, the device  100 , when installed in or used in the camera  900 , omits the model updater  110 . To illustrate, the remote computing device  150  of  FIG.  1    may generate the update model  106 . In such implementations, the update model  106  can be downloaded to the camera  900  for used by the SED engine  108 . 
       FIG.  10    illustrates a mobile device  1000  that incorporates aspects of the updated sound event detection model of  FIG.  1   ,  FIG.  3   , or  FIG.  4   . In  FIG.  10   , the mobile device  1000  includes or is coupled to the device  100  of  FIG.  1   . The mobile device  1000  includes a phone or tablet, as illustrative, non-limiting examples. The mobile device  1000  includes a display screen  1002  and one or more sensors  1004 , such as the microphone(s)  114  of  FIG.  1   . 
     During operation, the mobile device  1000  may perform particular actions in response to the device  100  detecting particular sound events. For example, the actions can include sending commands to other devices, such as a thermostat, a home automation system, another mobile device, etc. The sensor(s)  1004  enable detection of audio data, which the device  100  uses to detect sound events or to generate the update model  106 . 
     In the example illustrated in  FIG.  10   , the device  100  includes the base model  104 , the update model  106 , the SED engine  108 , and the model updater  110 . However, in other implementations, the device  100 , when installed in or used in the mobile device  1000 , omits the model updater  110 . To illustrate, the remote computing device  150  of  FIG.  1    may generate the update model  106 . In such implementations, the update model  106  can be downloaded to the mobile device  1000  for used by the SED engine  108 . 
       FIG.  11    illustrates an aerial device  1100  that incorporates aspects of the updated sound event detection model of  FIG.  1   ,  FIG.  3   , or  FIG.  4   . In  FIG.  11   , the aerial device  1100  includes or is coupled to the device  100  of  FIG.  1   . The aerial device  1100  is a manned, unmanned, or remotely piloted aerial device (e.g., a package delivery drone). The aerial device  1100  includes a control system  1102  and one or more sensors  1104 , such as the microphone(s)  114  of  FIG.  1   . The control system  1102  controls various operations of the aerial device  1100 , such as cargo release, sensor activation, take-off, navigation, landing, or combinations thereof. For example, the control system  1102  may control fly the aerial device  1100  between specified points and deployment of cargo at a particular location. In a particular aspect, the control system  1102  performs one or more action responsive to detection of a particular sound event by the device  100 . To illustrate, the control system  1102  may initiate a safe landing protocol in response to the device  100  detecting an aircraft engine. 
     In the example illustrated in  FIG.  11   , the device  100  includes the base model  104 , the update model  106 , the SED engine  108 , and the model updater  110 . However, in other implementations, the device  100 , when installed in or used in the aerial device  1100 , omits the model updater  110 . To illustrate, the remote computing device  150  of  FIG.  1    may generate the update model  106 . In such implementations, the update model  106  can be downloaded to the aerial device  1100  for used by the SED engine  108 . 
       FIG.  12    illustrates a headset  1200  that incorporates aspects of the updated sound event detection model of  FIG.  1   ,  FIG.  3   , or  FIG.  4   . In  FIG.  12   , the headset  1200  includes or is coupled to the device  100  of  FIG.  1   . The headset  1200  includes a microphone  1204  (e.g., one of the microphone(s)  114  of  FIG.  1   ) positioned to primarily capture speech of a user. The headset  1200  may also include one or more additional microphone positioned to primarily capture environmental sounds (e.g., for noise canceling operations). In a particular aspect, the headset  1200  performs one or more actions responsive to detection of a particular sound event by the device  100 . To illustrate, the headset  1200  may activate a noise cancellation feature in response to the device  100  detecting a gunshot. 
     In the example illustrated in  FIG.  12   , the device  100  includes the base model  104 , the update model  106 , the SED engine  108 , and the model updater  110 . However, in other implementations, the device  100 , when installed in or used in the headset  1200 , omits the model updater  110 . To illustrate, the remote computing device  150  of  FIG.  1    may generate the update model  106 . In such implementations, the update model  106  can be downloaded to the headset  1200  for used by the SED engine  108 . 
       FIG.  13    illustrates an appliance  1300  that incorporates aspects of the updated sound event detection model of  FIG.  1   ,  FIG.  3   , or  FIG.  4   . In  FIG.  13   , the appliance  1300  is a lamp; however, in other implementations, the appliance  1300  includes another Internet-of-Things appliance, such as a refrigerator, a coffee maker, an oven, another household appliance, etc. The appliance  1300  includes or is coupled to the device  100  of  FIG.  1   . The appliance  1300  includes one or more sensors  1304 , such as the microphone(s)  114  of  FIG.  1   . In a particular aspect, the appliance  1300  performs one or more actions responsive to detection of a particular sound event by the device  100 . To illustrate, the appliance  1300  may activate a light in response to the device  100  detecting a door closing. 
     In the example illustrated in  FIG.  13   , the device  100  includes the base model  104 , the update model  106 , the SED engine  108 , and the model updater  110 . However, in other implementations, the device  100 , when installed in or used in the appliance  1300 , omits the model updater  110 . To illustrate, the remote computing device  150  of  FIG.  1    may generate the update model  106 . In such implementations, the update model  106  can be downloaded to the appliance  1300  for used by the SED engine  108 . 
       FIG.  14    is a flow chart illustrating an example of a method  1400  of generating sound event detection data using an updated sound event detection model (e.g., the update model  106 ) of  FIG.  1   ,  FIG.  3   , or  FIG.  4   . The method  1400  can be initiated, controlled, or performed by the device  100 . For example, the processor(s)  120  or  132  of  FIG.  1    can execute instructions  124  from the memory  130  to cause the SED engine  108  to use the update model  106  to generate the output data  408  based on audio data samples corresponding to captured audio. 
     In block  1402 , the method  1400  includes receiving audio data samples. For example, the microphone(s)  114  of the device  100  can receive the audio data from an acoustic environment proximate the microphone(s)  114 , and the CODEC  142  or the SED engine  108  can generate the audio data samples based on the audio data. 
     In block  1404 , the method  1400  includes providing the audio data samples to a first neural network trained to generate a first output corresponding to a first count of classes of a first set of sound classes. For example, as illustrated in  FIG.  4   , the audio data samples  406  are provided (as one or more inputs  450 ) to the base model  104 . In this example, the base model  104  includes N nodes in the output layer  234 , where N is an integer corresponding to the count of the set of sound classes that the base model  104  is configured to detect. The output layer  234  generates the first output  452 , which including one data element per node of the output layer  234  (e.g., N data elements). 
     In block  1406 , the method  1400  includes providing the audio data samples to a second neural network to generate a second output corresponding to a second count of classes of a second set of sound classes, where the second count of classes is greater than the first count of classes. For example, as illustrated in  FIG.  4   , the audio data samples  406  are provided to the incremental model  302 . In this example, the output layer  322  of the incremental model  302  includes N+K nodes, where K is an integer greater than or equal to one and corresponds to the count of sound classes that the update model  106  can detect that are not detected by the base model  104 . Thus, N+K is greater than N. The output layer  322  generates an output including one data element per node of the output layer  322  (e.g., N+K data elements). 
     In block  1408 , the method  1400  includes providing the first output to a neural adapter to generate a third output corresponding to the second count of classes of the second set of sound classes. For example, the base model  104  of  FIG.  4    provides an output to the neural adapter  310 . The neural adapter  310  generates an output that has the same number of data elements as the output of the incremental model  302  (e.g., N+K data elements). 
     In block  1410 , the method  1400  includes providing the second output and the third output to a merger adapter to generate sound identification data based on the audio data samples. For example, in  FIG.  4   , the neural adapter  310  generates the third output  456  and the incremental model  302  generates the second output  454 . In this example, the third output  456  and the second output  454  are each provided the merger adapter  308 . The aggregation layer  316  of the merger adapter  308  combines the outputs  454  and  456  to generate a merged output  458 , and the output layer  320  of the merger adapter  308  generates the output data  408  based on the merged output  458 . The output data  408  includes an indication of a recognized sound event (e.g., the sound event identification data  460 ). 
       FIG.  15    is a flow chart illustrating an example of a method  1500  of generating the updated sound event detection model of  FIG.  1   ,  FIG.  3   , or  FIG.  4   . The method  1500  can be initiated, controlled, or performed by the device  100 . For example, the processor(s)  120  or  132  of  FIG.  1    can execute instructions  124  from the memory  130  to cause the model updater  110  to generate the update model  106  based on the audio data samples  126  and  128 . Alternatively, in a particular aspect, the method  1500  can be initiated, controlled, or performed by the remote computing device  150  of  FIG.  1   . To illustrate, the model updater  110  may be executed at the remote computing device  150  using audio data samples  126  from the base training data  152  and audio data samples  128  sent to the remote computing device  150  from the device  100 . 
     In block  1502 , the method  1500  includes generating a second neural network based on a first neural network of a first sound event classifier. The first neural network includes an input layer, hidden layers, and a first output layer, and the second neural network includes a copy of the input layer of the first neural network, a copy of the hidden layers of the first neural network, and a second output layer coupled to the copy of the hidden layers. The first output layer includes a first count of output nodes and the second output layer includes a second count of output node, where the second count of output nodes is greater than the first count of output nodes. For example, the model updater  110  generates the incremental model  302  by duplicating (e.g., copying) the base model  104 , which includes the input layer  204 , the hidden layers  206 , and the output layer  234 , and replacing or modifying the output layer  234  with the output layer  322 . In this example, the base model  104  is a first neural network that is trained to generate sound identification data in response to input of audio data samples representing one or more of a first set of sound classes, and the output layer  234  of the base model  104  includes a count of nodes (e.g., N nodes) corresponding to a number of classes of the first set of sound classes. Further, the incremental model  302  is a second neural network that is to be trained to generate sound identification data in response to input of audio data samples representing one or more of a second set of sound classes and the output layer  322  of the incremental model  302  includes a second count of nodes (e.g., N+K) corresponding to a second number of classes of the second set of sound classes. The second set of count classes includes the first set of sound classes and one or more additional sound classes. 
     In block  1504 , the method  1500  includes linking the first neural network and the second neural network to one or more adapter networks. For example, the model updater  110  of  FIG.  1    generates the adapter network(s)  314 , and links outputs of the base model  104  (e.g., the first neural network) and the incremental model  302  (e.g., the second neural network) to the adapter network(s)  314 . 
     In block  1506 , the method  1500  includes providing labeled training data as input to the first neural network and to the second neural network. For example, in  FIG.  3   , the model updater  110  provides the labeled training data  304  as one or more inputs  350  to the base model  104  and to the incremental model  302 . 
     In block  1508 , the method  1500  includes modifying output of the first neural network and the second neural network via the one or more adapter networks. For example, in response to the labeled training data  304 , the base model  104  and the incremental model  302  of  FIG.  3    each provide output to the adapter network(s)  314 . To illustrate, the base model  104  provides the first output(s)  352  to the neural adapter  310 , and the neural adapter generates the third output(s)  356  based on the first output(s)  352 . Additionally, the incremental model  302  generates the second output(s)  354 . The third output(s)  356  and the second output(s)  354  are provided to the merger adapter  308 , and the merger adapter  308  generates the output data  318 . 
     In block  1510 , the method  1500  training a second sound event classifier by updating link weights of the second neural network and of the one or more adapter networks based on output of the adapter networks and labels of the labeled training data. For example, the model updater  110  trains the update model  106  by determining the updated link weights  362  (e.g., using gradient descent or another optimization search process) and providing the updated link weights  362  to one or more of the incremental model  302 , the neural adapter  310 , and the merger adapter  308 . In this example, the base link weights  238  of the base model  104  are not changed. 
     In conjunction with the described implementations, an apparatus includes means for storing a sound event classifier trained to generate first sound identification data in response to input of audio data representing one or more of a first set of sound classes. For example, the means for storing includes the remote computing device  150 , the device  100 , the memory  130 , the processor  120 , the processor(s)  132 , one or more other circuits or components configured to store a trained sound event classifier (e.g., a neural network), or any combination thereof. 
     The apparatus also includes means for generating an updated sound event classifier trained to generate second sound identification data in response to input of audio data samples representing one or more of a second set of sound classes, where the second set of count classes includes the first set of sound classes and one or more additional sound classes. For example, the means for generating the updated sound event classifier includes the remote computing device  150 , the device  100 , the instructions  124 , the processor  120 , the processor(s)  132 , the model updater  110 , one or more other circuits or components configured to generate an updated sound event classifier trained to generate second sound identification data in response to input of audio data representing one or more of a second set of sound classes, where the second set of count classes includes the first set of sound classes and one or more additional sound classes, or any combination thereof. 
     In conjunction with the described implementations, an apparatus includes means for generating a first output corresponding to a first count of classes of a first set of sound classes. For example, the means generating a first output includes the processor  120 , the processor(s)  132 , the base model  104 , the update model  106 , the SED engine  108 , one or more other circuits or components configured to generate a first output corresponding to a first count of classes of a first set of sound classes, or any combination thereof. 
     The apparatus also includes means for generating a second output corresponding to a second count of classes of a second set of sound classes, the second count of classes greater than the first count of classes. For example, the means for generating a second output includes the processor  120 , the processor(s)  132 , the incremental model  302 , the update model  106 , the SED engine  108 , one or more other circuits or components configured to generate a second output corresponding to a second count of classes of a second set of sound classes, or any combination thereof. 
     The apparatus also includes means for generating a third output based on the first output, the third output corresponding to the second count of classes of the second set of sound classes. For example, the means for generating a third output includes the processor  120 , the processor(s)  132 , the update model  106 , the adapter network(s)  314 , the neural adapter  310 , the SED engine  108 , one or more other circuits or components configured to generate a third output based on the first output, or any combination thereof. 
     The apparatus also includes means for generating sound event identification data based on the third output and the second output. For example, the means for generating sound identification data based on the third output and the second output includes the processor  120 , the processor(s)  132 , the update model  106 , the adapter network(s)  314 , the merger adapter  308 , the SED engine  108 , one or more other circuits or components configured to generate sound identification data based on the third output and the second output, or any combination thereof. 
     Those of skill would further appreciate that the various illustrative logical blocks, configurations, modules, circuits, and algorithm steps described in connection with the implementations disclosed herein may be implemented as electronic hardware, computer software executed by a processor, or combinations of both. Various illustrative components, blocks, configurations, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or processor executable instructions depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, such implementation decisions are not to be interpreted as causing a departure from the scope of the present disclosure. 
     The steps of a method or algorithm described in connection with the implementations disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disk, a removable disk, a compact disc read-only memory (CD-ROM), or any other form of non-transient storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor may read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an application-specific integrated circuit (ASIC). The ASIC may reside in a computing device or a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a computing device or user terminal. 
     Particular aspects of the disclosure are described below in a first set of interrelated clauses: 
     According to Clause 1, a device includes a processor configured to receive audio data samples; provide the audio data samples to a first neural network trained to generate a first output corresponding to a first count of classes of a first set of sound classes; provide the audio data samples to a second neural network to generate a second output corresponding to a second count of classes of a second set of sound classes, the second count of classes greater than the first count of classes; provide the first output to a neural adapter to generate a third output corresponding to the second count of classes of the second set of sound classes; and provide the second output and the third output to a merger adapter to generate sound event identification data based on the audio data samples. 
     Clause 2 includes the device of Clause 1 wherein the first neural network has a base topology and a first output layer and the second neural network has the base topology and a second output layer, and wherein the first output layer includes a first count of nodes, the second output layer includes a second count of nodes, and the second count of nodes is greater than the first count of nodes. 
     Clause 3 includes the device of Clause 2 wherein the neural adapter has an input layer including the first count of nodes and an output layer including the second count of nodes. 
     Clause 4 includes the device of any of Clauses 1 to 3 wherein the merger adapter is configured to merge the second output and the third output, element-by-element, to form a merged output. 
     Clause 5 includes the device of Clause 4 wherein the merger adapter is configured to generate output data including the sound event identification data based on the merged output. 
     Clause 6 includes the device of any of Clauses 1 to 5 wherein the audio data samples include features extracted from audio data. 
     Clause 7 includes the device of any of Clauses 1 to 6 wherein the audio data samples include Mel spectrum features extracted from audio data. 
     Clause 8 includes the device any of Clauses 1 to 7 and further includes one or more microphones coupled to the processor and configured to capture audio data to generate the audio data samples. 
     Clause 9 includes the device of Clause 8 wherein the processor and the one or more microphones are integrated within a mobile computing device and the audio data represents an acoustic environment of the mobile computing device. 
     Clause 10 includes the device of Clause 8 wherein the processor and the one or more microphones are integrated within a vehicle. 
     Clause 11 includes the device of Clause 8 wherein the processor and the one or more microphones are integrated within a wearable device and the audio data represents an acoustic environment of the wearable device. 
     Clause 12 includes the device of Clause 8 wherein the processor and the one or more microphones are integrated within a headset. 
     Clause 13 includes the device of Clause 8 wherein the processor is included in an integrated circuit. 
     Particular aspects of the disclosure are described below in a second set of interrelated clauses: 
     According to Clause 14, a method includes receiving audio data samples; providing, by a processor, the audio data samples to a first neural network trained to generate a first output corresponding to a first count of classes of a first set of sound classes; providing, by the processor, the audio data samples to a second neural network to generate a second output corresponding to a second count of classes of a second set of sound classes, the second count of classes greater than the first count of classes; providing, by the processor, the first output to a neural adapter to generate a third output corresponding to the second count of classes of the second set of sound classes; and providing, by the processor, the second output and the third output to a merger adapter to generate sound event identification data based on the audio data samples. 
     Clause 15 includes the method of Clause 14 wherein the first neural network has a base topology and a first output layer and the second neural network has the base topology and a second output layer, and wherein the first output layer includes a first count of nodes, the second output layer includes a second count of nodes, and the second count of nodes is greater than the first count of nodes. 
     Clause 16 includes the method of Clause 15 wherein the neural adapter has an input layer including the first count of nodes and an output layer including the second count of nodes. 
     Clause 17 includes the method of any of Clauses 14 to 16 wherein the merger adapter merges the second output and the third output, element-by-element, to form a merged output. 
     Clause 18 includes the method of Clause 17 wherein merger adapter generates output data including the sound event identification data based on the merged output. 
     Clause 19 includes the method of any of Clauses 14 to 18 and further includes generating the audio data samples by extracting features from the audio data representing an acoustic environment. 
     Clause 20 includes the method of any of Clauses 14 to 19 and further includes capturing audio data at one or more microphones coupled to the processor, wherein the audio data samples are generated based on the captured audio data. 
     Clause 21 includes the method of any of Clauses 14 to 20 and further includes performing an action responsive to the sound event identification data. 
     Particular aspects of the disclosure are described below in a third set of interrelated clauses: 
     According to Clause 22, a non-transitory computer-readable storage medium includes instructions that when executed by a processor, cause the processor to provide audio data samples to a first neural network trained to generate a first output corresponding to a first count of classes of a first set of sound classes; provide the audio data samples to a second neural network to generate a second output corresponding to a second count of classes of a second set of sound classes, the second count of classes greater than the first count of classes; provide the first output to a neural adapter to generate a third corresponding to the second count of classes of the second set of sound classes; and provide the second output and the third output to a merger adapter to generate sound event identification data based on the audio data samples. 
     Clause 23 includes the non-transitory computer-readable storage medium of Clause 22 wherein the first neural network has a base topology and a first output layer and the second neural network has the base topology and a second output layer, and wherein the first output layer includes a first count of nodes, the second output layer includes a second count of nodes, and the second count of nodes is greater than the first count of nodes. 
     Clause 24 includes the non-transitory computer-readable storage medium of Clause 22 or Clause 23 wherein the instructions when executed by the processor further cause the processor to perform an action responsive to the sound event identification data. 
     Clause 25 includes the non-transitory computer-readable storage medium of any of Clauses 22 to 24 wherein the merger adapter generates the sound event identification data based on merged output based on element-by-element merger of the third output and the second output. 
     Particular aspects of the disclosure are described below in a fourth set of interrelated clauses: 
     According to Clause 26, a device includes means for generating a first output based on audio data samples, the first output having a first count of data elements corresponding to a first count of classes of a first set of sound classes; means for generating a second output based on the audio data samples, the second output corresponding to a second count of classes of a second set of sound classes, the second count of classes greater than the first count of classes; means for generating a third output based on the first output, the third output corresponding to the second count of classes of the second set of sound classes; and means for generating sound event identification data based on the third output and the second output. 
     Clause 27 includes the device of Clause 26 wherein the means for generating the third output based on the first output comprises an input layer including a first count of nodes and an output layer including a second count of nodes, and wherein the first count of nodes corresponds to the first count of classes, and the second count of nodes corresponds to the second count of classes. 
     Clause 28 includes the device of Clause 26 or Clause 27 wherein the means for generating the sound event identification data based on the third output and the second output is configured to merge the second output and the third output, element-by-element, to form a merged output. 
     Clause 29 includes the device of any of Clauses 26 to 28 wherein the means for generating the sound event identification data based on the third output and the second output is configured to generate output data including the sound event identification data based on a merged output formed from the third output and the second output. 
     Clause 30 includes the device of any of Clauses 26 to 29 further comprising means for capturing audio data, wherein the audio data samples include features extracted from the audio data. 
     Particular aspects of the disclosure are described below in a fifth set of interrelated clauses: 
     According to Clause 31, a device includes a memory storing a sound event classifier trained to generate first sound identification data in response to input of audio data representing one or more of a first set of sound classes. The device also includes a processor coupled to the memory and configured to generate an updated sound event classifier trained to generate second sound identification data in response to input of audio data representing one or more of a second set of sound classes, the second set of sound classes including the first set of sound classes and one or more additional sound classes. The updated sound event classifier includes the sound event classifier, a second sound event classifier, a neural adapter, and a merger adapter. The neural adapter includes one or more adapter layers configured to receive an input having a count of data elements corresponding to an output layer of the sound event classifier and configured to generate an output having a second count of data elements corresponding to an output layer of the second sound event classifier. The merger adapter includes one or more aggregation layers configured to merge the output from the neural adapter and an output of the second sound event classifier and including an output layer to generate the second sound identification data. 
     Clause 32 includes the device of Clause 31 and further includes one or more microphones coupled to the processor and configured to receive audio data corresponding to the additional sound classes. 
     Clause 33 includes the device of Clause 31 or Clause 32 and further includes one or more input devices coupled to the processor and configured to receive label data associated with the additional sound classes. 
     Clause 34 includes the device of any of Clauses 31 to 33 wherein the memory stores instructions corresponding to a model updater, and wherein the model updater is executable by the processor to generate the updated sound event classifier based on the sound event classifier, the first set of sound classes, and the additional sound classes. 
     Clause 35 includes the device of any of Clauses 31 to 34 wherein the processor and the memory are integrated within a mobile computing device. 
     Clause 36 includes the device of Clauses 31 to 34 wherein the processor and the memory are integrated within a vehicle. 
     Clause 37 includes the device of Clauses 31 to 34 wherein the processor and the memory are integrated within wearable device. 
     Clause 38 includes the device of Clauses 31 to 34 wherein the processor and the memory are integrated within an augmented reality headset, a mixed reality headset, or a virtual reality headset. 
     Clause 39 includes the device of Clauses 31 to 38 wherein the processor is included in an integrated circuit. 
     Clause 40 includes the device of Clauses 31 to 39 and further includes one or more output devices coupled to the processor and configured to generate a sound classification output based on the second sound identification data. 
     Particular aspects of the disclosure are described below in a sixth set of interrelated clauses: 
     According to Clause 41, a device includes means for storing a sound event classifier trained to generate first sound identification data in response to input of audio data representing one or more of a first set of sound classes, and includes means for generating an updated sound event classifier trained to generate second sound identification data in response to input of audio data representing one or more of a second set of sound classes; the second set of sound classes including the first set of sound classes and one or more additional sound classes. The updated sound event classifier includes the sound event classifier, a second sound event classifier, a neural adapter, and a merger adapter. The neural adapter includes one or more adapter layers configured to receive an input having a count of data elements corresponding to an output layer of the sound event classifier and configured to generate an output having a second count of data elements corresponding to an output layer of the second sound event classifier. The merger adapter includes one or more aggregation layers configured to merge the output from the neural adapter and an output of the second sound event classifier and including an output layer to generate the second sound identification data. 
     Clause 42 includes the device of Clause 41 and further includes means for receiving audio data corresponding to the additional sound classes. 
     Clause 43 includes the device of Clause 41 or Clause 42 and further includes means for receiving label data associated with the additional sound classes. 
     Clause 44 includes the device of any of Clauses 41 to 43 wherein the means for storing and the means for generating are integrated within a mobile computing device. 
     Clause 45 includes the device of any of Clauses 41 to 43 wherein the means for storing and the means for generating are integrated within a vehicle. 
     Clause 46 includes the device of any of Clauses 41 to 43 wherein the means for storing and the means for generating are integrated within wearable device. 
     Clause 47 includes the device of any of Clauses 41 to 43 wherein the means for storing and the means for generating are integrated within an augmented reality or virtual reality headset. 
     Particular aspects of the disclosure are described below in a seventh set of interrelated clauses: 
     According to Clause 48, a method includes generating a second neural network based on a first neural network of a first sound event classifier, wherein the first neural network includes an input layer, hidden layers, and a first output layer, and the second neural network includes a copy of the input layer of the first neural network, a copy of the hidden layers of the first neural network, and a second output layer coupled to the copy of the hidden layers. The first output layer includes a first count of output nodes and the second output layer includes a second count of output nodes, wherein the second count of output nodes is greater than the first count of output nodes. The method also includes linking the first neural network and the second neural network to one or more adapter networks; providing labeled training data as input to the first neural network and to the second neural network; modifying output of the first neural network and the second neural network via the one or more adapter networks; and training a second sound event classifier by updating link weights of the second neural network and of the one or more adapter networks based on output of the adapter networks and labels of the labeled training data. 
     Clause 49 includes the method of Clause 48 wherein the first count of output nodes corresponds to a first set of sound classes that the first sound event classifier is trained to detect and the second count of output nodes corresponds to a second set of sound classes that the second sound event classifier is trained to detect, and wherein the second set of sound classes includes the first set of sound classes and one or more additional sound classes. 
     Clause 50 includes the method of Clause 49 and further includes capturing, by a microphone of a mobile device, audio data representing a sound event of the one or more additional sound classes, wherein a processor of the mobile device trains the second sound event classifier based in part on audio data samples representing the audio data captured by the microphone of the mobile device. 
     Clause 51 includes the method of Clause 49 or Clause 50 wherein the labeled training data includes one or more audio data samples representing each class of the first set of sound classes and one or more audio data samples representing each class of the one or more additional sound classes. 
     Clause 52 includes the method of any of Clauses 48 to 51 wherein the adapter networks include a neural adapter including one or more adapter layers configured to receive an input having a first count of data elements and configured to generate an output having a second count of data elements, the first count of data elements corresponding to the first count of output nodes and the second count of data elements corresponding to the second count of output nodes. 
     Clause 53 includes the method of Clause 52 wherein the adapter networks include a merger adapter including one or more aggregation layers and an output layer, wherein the one or more aggregation layers are configured to merge the output from the neural adapter and an output of the second neural network, and wherein the output layer is configured to generate output data identifying a sound event. 
     Clause 54 includes the method of any of Clauses 48 to 53 and further including, after training the second sound event classifier, proving audio data samples as input to the second sound event classifier and generating output data identifying a sound event detected in the audio data samples by the second sound event classifier. 
     Clause 55 includes the method of Clause 54 wherein generating the output data identifying the sound event includes providing the audio data samples to the first neural network to generate a first output; providing the audio data samples to the second neural network to generate a second output; providing the first output to a neural adapter of the one or more adapter networks to generate a third output; and providing the second output and the third output to a merger adapter of the one or more adapter networks to generate the output data. 
     Clause 56 includes the method of any of Clauses 48 to 55 wherein link weights of the first neural network are not updated during the training of the second sound event classifier. 
     Clause 57 includes the method of any of Clauses 48 to 56 wherein the second sound event classifier includes the first neural network, the second neural network, and the one or more adapter networks. 
     Particular aspects of the disclosure are described below in an eighth set of interrelated clauses: 
     According to Clause 58, a non-transitory computer-readable storage medium includes instructions that when executed by a processor, cause the processor to generate a second neural network based on a first neural network of a first sound event classifier, wherein the first neural network includes an input layer, hidden layers, and a first output layer, and the second neural network includes a copy of the input layer of the first neural network, a copy of the hidden layers of the first neural network, and a second output layer coupled to the copy of the hidden layers. The first output layer includes a first count of output nodes and the second output layer includes a second count of output node, wherein the second count of output nodes is greater than the first count of output nodes. The instructions further cause the processor to link the first neural network and the second neural network to one or more adapter networks; provide labeled training data as input to the first neural network and to the second neural network; modify output of the first neural network and the second neural network via the one or more adapter networks; and train a second sound event classifier by updating link weights of the second neural network and of the one or more adapter networks based on output of the adapter networks and labels of the labeled training data. 
     Clause 59 includes the non-transitory computer-readable storage medium of Clause 58 wherein the first sound event classifier is trained to detect a first set of sound classes and the second sound event classifier is trained to detect a second set of sound classes, the second set of sound classes including the first set of sound classes and one or more additional sound classes. 
     Clause 60 includes the non-transitory computer-readable storage medium of Clause 58 or Clause 59 wherein the instructions when executed by the processor further cause the processor to, after training the second sound event classifier, provide audio data samples representing capture audio data as input to the second sound event classifier and generate output data identifying a sound event detected in the audio data samples by the second sound event classifier. 
     The previous description of the disclosed aspects is provided to enable a person skilled in the art to make or use the disclosed aspects. Various modifications to these aspects will be readily apparent to those skilled in the art, and the principles defined herein may be applied to other aspects without departing from the scope of the disclosure. Thus, the present disclosure is not intended to be limited to the aspects shown herein but is to be accorded the widest scope possible consistent with the principles and novel features as defined by the following claims.