Patent Publication Number: US-11030479-B2

Title: Mapping visual tags to sound tags using text similarity

Description:
FIELD 
     The application relates generally to technically inventive, non-routine solutions that are necessarily rooted in computer technology and that produce concrete technical improvements. 
     BACKGROUND 
     Machine learning, sometimes referred to as deep learning, can be used for a variety of useful applications related to data understanding, detection, and/or classification. In computer simulation industries such as gaming industries, video and audio are two separate processes. Simulations are first designed and produced without audio, and then audio groups investigate the simulation videos and insert the corresponding sound effects (SFX) from the SFX database, which is time-consuming. 
     SUMMARY 
     As understood herein, machine learning may be used to address the technical problem noted above by providing SFX recommendations that are relevant to computer simulation scenes. 
     Accordingly, an apparatus includes at least one processor and at least one computer storage that is not a transitory signal and that includes instructions executable by the processor to classify sound effects (SFX) to render classified SFX. The instructions also are executable to semantically match SFX labels of the classified SFX with visual labels derived from video recognition of attributes in at least a first video to incorporate, into the first video, classified SFX associated with the SFX labels. 
     In some embodiments, the instructions may be executable to generate the visual labels based on recognition of at least one object in the first video, and/or based on recognition of at least one action in the first video, and/or based on recognition of at least one caption in the first video. 
     In example implementations, the instructions may be executable to semantically match SFX labels with visual labels using text similarity between the SFX labels and visual labels. 
     In some embodiments, the instructions are executable to derive a first numerical vector corresponding to the visual labels, derive a second numerical vector corresponding to the SFX labels, and determine a similarity of the SFX labels to the visual labels at least in part by computing a distance between the first and second vectors. In such embodiments, the instructions may be executable to determine that a first SFX label is more similar to a visual label than is a second SFX label responsive to a determination that a distance between the first vector and a second vector associated with the first SFX label is smaller than distance between the first vector and a second vector associated with the second SFX label. 
     If desired, the instructions may be executable to map a single visual label to plural SFX labels. 
     In another aspect, a method includes generating at least one visual tag describing at least one attribute of at least a first video and associating with the first video at least one sound effect (SFX) associated with at least one SFX tag at least in part based on a semantic similarity between the visual tag and the SFX tag. 
     In another aspect, an assembly includes at least one computer storage that is not a transitory signal and that in turn includes instructions executable by at least one processor for identifying at least one visual tag describing at least a first video. The instructions also are executable for associating with the first video at least one sound effect (SFX) associated with at least one SFX tag at least in part based on a similarity between the visual tag and the SFX tag. 
     The details of the present application, both as to its structure and operation, can best be understood in reference to the accompanying drawings, in which like reference numerals refer to like parts, and in which: 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of an example system consistent with present principles; 
         FIG. 2  is a flow chart of example overall logic for recommending sound effects (SFX) for a video or computer simulation consistent with present principles; 
         FIG. 3  is a block diagram illustrating the logic of  FIG. 2 ; 
         FIG. 4  is a block diagram of a first approach for SFX tagging involving direct mapping from video to SFX tags; 
         FIG. 5  is a block diagram of additional features consistent with the first approach in  FIG. 4 ; 
         FIGS. 6 and 7  are block diagrams of machine learning architectures related to “noisy” coarse-grained (in the example shown, 32-category) SFX classification consistent with the first approach in  FIG. 4 ; 
         FIG. 8  is a block diagram of a semi-supervised machine learning architecture related to “noisy” fine grain SFX classification consistent with the first approach in  FIG. 4 ; 
         FIG. 9  is a block diagram of a machine learning architecture related to training and testing phases consistent with the first approach in  FIG. 4 ; 
         FIG. 10  is a block diagram of features of a second approach for video tagging involving indirect tagging by visual understanding; and 
         FIGS. 11 and 12  are screen shots and related tables illustrating correlating visual tags with matching SFX audio tags. 
     
    
    
     DETAILED DESCRIPTION 
     In accordance with present principles, deep learning-based domain adaptation methods may be used to recommend SFX for videos and computer simulations such as video games. 
     The methods described herein may concern multiple objects and multiple actions associated with the multiple objects. For example, an image text-block of many texts may be an “object”, and the type of the image block may be an “action”. 
     This disclosure also relates generally to computer ecosystems including aspects of consumer electronics (CE) device networks such as but not limited to distributed computer game networks, augmented reality (AR) networks, virtual reality (VR) networks, video broadcasting, content delivery networks, virtual machines, and artificial neural networks and machine learning applications. 
     A system herein may include server and client components, connected over a network such that data may be exchanged between the client and server components. The client components may include one or more computing devices including AR headsets, VR headsets, game consoles such as Sony PlayStation® and related motherboards, game controllers, portable televisions (e.g. smart TVs, Internet-enabled TVs), portable computers such as laptops and tablet computers, and other mobile devices including smart phones and additional examples discussed below. These client devices may operate with a variety of operating environments. For example, some of the client computers may employ, as examples, Orbis or Linux operating systems, operating systems from Microsoft, or a Unix operating system, or operating systems produced by Apple, Inc. or Google. These operating environments may be used to execute one or more programs/applications, such as a browser made by Microsoft or Google or Mozilla or other browser program that can access websites hosted by the Internet servers discussed below. Also, an operating environment according to present principles may be used to execute one or more computer game programs/applications and other programs/applications that undertake present principles. 
     Servers and/or gateways may include one or more processors executing instructions that configure the servers to receive and transmit data over a network such as the Internet. Additionally, or alternatively, a client and server can be connected over a local intranet or a virtual private network. A server or controller may be instantiated by a game console and/or one or more motherboards thereof such as a Sony PlayStation®, a personal computer, etc. 
     Information may be exchanged over a network between the clients and servers. To this end and for security, servers and/or clients can include firewalls, load balancers, temporary storages, and proxies, and other network infrastructure for reliability and security. One or more servers may form an apparatus that implement methods of providing a secure community such as an online social website or video game website to network users to communicate crowdsourced in accordance with present principles. 
     As used herein, instructions refer to computer-implemented steps for processing information in the system. Instructions can be implemented in software, firmware or hardware and include any type of programmed step undertaken by components of the system. 
     A processor may be single- or multi-chip processor that can execute logic by means of various lines such as address lines, data lines, and control lines and registers and shift registers. 
     Software modules described by way of the flow charts and user interfaces herein can include various sub-routines, procedures, etc. Without limiting the disclosure, logic stated to be executed by a particular module can be redistributed to other software modules and/or combined together in a single module and/or made available in a shareable library. 
     As indicated above, present principles described herein can be implemented as hardware, software, firmware, or combinations thereof; hence, illustrative components, blocks, modules, circuits, and steps are set forth in terms of their functionality. 
     Further to what has been alluded to above, logical blocks, modules, and circuits described below can be implemented or performed with a general-purpose processor, a digital signal processor (DSP), a field programmable gate array (FPGA) or other programmable logic device such as an application specific integrated circuit (ASIC), discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A processor can be implemented by a controller or state machine or a combination of computing devices. 
     The functions and methods described below may be implemented in hardware circuitry or software circuitry. When implemented in software, the functions and methods can be written in an appropriate language such as but not limited to Java, C # or C++, and can be stored on or transmitted through a computer-readable storage medium such as a random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), compact disk read-only memory (CD-ROM) or other optical disk storage such as digital versatile disc (DVD), magnetic disk storage or other magnetic storage devices including removable thumb drives, etc. A connection may establish a computer-readable medium. Such connections can include, as examples, hard-wired cables including fiber optics and coaxial wires and digital subscriber line (DSL) and twisted pair wires. Such connections may include wireless communication connections including infrared and radio. 
     Components included in one embodiment can be used in other embodiments in any appropriate combination. For example, any of the various components described herein and/or depicted in the Figures may be combined, interchanged or excluded from other embodiments. 
     “A system having at least one of A, B, and C” (likewise “a system having at least one of A, B, or C” and “a system having at least one of A, B, C”) includes systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc. 
     Now specifically referring to  FIG. 1 , an example system  10  is shown, which may include one or more of the example devices mentioned above and described further below in accordance with present principles. The first of the example devices included in the system  10  is a consumer electronics (CE) device such as an audio video device (AVD)  12  such as but not limited to an Internet-enabled TV with a TV tuner (equivalently, set top box controlling a TV). However, the AVD  12  alternatively may be an appliance or household item, e.g. computerized Internet enabled refrigerator, washer, or dryer. The AVD  12  alternatively may also be a computerized Internet enabled (“smart”) telephone, a tablet computer, a notebook computer, an augmented reality (AR) headset, a virtual reality (VR) headset, Internet-enabled or “smart” glasses, another type of wearable computerized device such as a computerized Internet-enabled watch, a computerized Internet-enabled bracelet, a computerized Internet-enabled music player, computerized Internet-enabled head phones, a computerized Internet-enabled implantable device such as an implantable skin device, other computerized Internet-enabled devices, etc. Regardless, it is to be understood that the AVD  12  is configured to undertake present principles (e.g., communicate with other consumer electronics (CE) devices to undertake present principles, execute the logic described herein, and perform any other functions and/or operations described herein). 
     Accordingly, to undertake such principles the AVD  12  can be established by some or all of the components shown in  FIG. 1 . For example, the AVD  12  can include one or more displays  14  that may be implemented by a high definition or ultra-high definition “4K” or higher flat screen and that may be touch-enabled for receiving user input signals via touches on the display. The AVD  12  may include one or more speakers  16  for outputting audio in accordance with present principles, and at least one additional input device  18  such as an audio receiver/microphone for entering audible commands to the AVD  12  to control the AVD  12 . The example AVD  12  may also include one or more network interfaces  20  for communication over at least one network  22  such as the Internet, an WAN, an LAN, etc. under control of one or more processors. Thus, the interface  20  may be, without limitation, a Wi-Fi transceiver, which is an example of a wireless computer network interface, such as but not limited to a mesh network transceiver. Furthermore, note the network interface  20  may be, e.g., a wired or wireless modem or router, or other appropriate interface such as, for example, a wireless telephony transceiver, or Wi-Fi transceiver as mentioned above, etc. 
     It is to be understood that the one or more processors control the AVD  12  to undertake present principles, including the other elements of the AVD  12  described herein such as controlling the display  14  to present images thereon and receiving input therefrom. The one or more processors may include a central processing unit (CPU)  24  as well as a graphics processing unit (GPU)  25  on a graphics card  25 A. 
     In addition to the foregoing, the AVD  12  may also include one or more input ports  26  such as, e.g., a high definition multimedia interface (HDMI) port or a USB port to physically connect (e.g., using a wired connection) to another consumer electronics (CE) device and/or a headphone port to connect headphones to the AVD  12  for presentation of audio from the AVD  12  to a user through the headphones. For example, the input port  26  may be connected via wire or wirelessly to a cable or satellite source  26   a  of audio video content. Thus, the source  26   a  may be, e.g., a separate or integrated set top box, or a satellite receiver. Or, the source  26   a  may be a game console or disk player containing content that might be regarded by a user as a favorite for channel assignation purposes. The source  26   a  when implemented as a game console may include some or all of the components described below in relation to the CE device  44  and may implement some or all of the logic described herein. 
     The AVD  12  may further include one or more computer memories  28  such as disk-based or solid-state storage that are not transitory signals, in some cases embodied in the chassis of the AVD as standalone devices or as a personal video recording device (PVR) or video disk player either internal or external to the chassis of the AVD for playing back AV programs or as removable memory media. Also in some embodiments, the AVD  12  can include a position or location receiver such as but not limited to a cellphone receiver, GPS receiver and/or altimeter  30  that is configured to, e.g., receive geographic position information from at least one satellite or cellphone tower and provide the information to the processor  24  and/or determine an altitude at which the AVD  12  is disposed in conjunction with the processor  24 . However, it is to be understood that that another suitable position receiver other than a cellphone receiver, GPS receiver and/or altimeter may be used in accordance with present principles to, for example, determine the location of the AVD  12  in all three dimensions. 
     Continuing the description of the AVD  12 , in some embodiments the AVD  12  may include one or more cameras  32  that may be, e.g., a thermal imaging camera, a digital camera such as a webcam, an infrared (IR) camera, and/or a camera integrated into the AVD  12  and controllable by the processor  24  to generate pictures/images and/or video in accordance with present principles. Also included on the AVD  12  may be a Bluetooth transceiver  34  and other Near Field Communication (NFC) element  36  for communication with other devices using Bluetooth and/or NFC technology, respectively. An example NFC element can be a radio frequency identification (RFID) element. 
     Further still, the AVD  12  may include one or more auxiliary sensors  37  (e.g., a motion sensor such as an accelerometer, gyroscope, cyclometer, or a magnetic sensor, an infrared (IR) sensor, an optical sensor, a speed and/or cadence sensor, a gesture sensor (e.g., for sensing gesture command), etc.) providing input to the processor  24 . The AVD  12  may include an over-the-air TV broadcast port  38  for receiving OTA TV broadcasts providing input to the processor  24 . In addition to the foregoing, it is noted that the AVD  12  may also include an infrared (IR) transmitter and/or IR receiver and/or IR transceiver  42  such as an IR data association (IRDA) device. A battery (not shown) may be provided for powering the AVD  12 . 
     Still referring to  FIG. 1 , in addition to the AVD  12 , the system  10  may include one or more other consumer electronics (CE) device types. In one example, a first CE device  44  may be used to send computer game audio and video to the AVD  12  via commands sent directly to the AVD  12  and/or through the below-described server while a second CE device  46  may include similar components as the first CE device  44 . In the example shown, the second CE device  46  may be configured as an AR or VR headset worn by a user  47  as shown. In the example shown, only two CE devices  44 ,  46  are shown, it being understood that fewer or greater devices may also be used in accordance with present principles. 
     In the example shown, all three devices  12 ,  44 ,  46  are assumed to be members of a network such as a secured or encrypted network, an entertainment network or Wi-Fi in, e.g., a home, or at least to be present in proximity to each other in a certain location and able to communicate with each other and with a server as described herein. However, present principles are not limited to a particular location or network unless explicitly claimed otherwise. 
     The example non-limiting first CE device  44  may be established by any one of the above-mentioned devices, for example, a smart phone, a digital assistant, a portable wireless laptop computer or notebook computer or game controller (also referred to as “console”), and accordingly may have one or more of the components described below. The second CE device  46  without limitation may be established by an AR headset, a VR headset, “smart” Internet-enabled glasses, or even a video disk player such as a Blu-ray player, a game console, and the like. Still further, in some embodiments the first CE device  44  may be a remote control (RC) for, e.g., issuing AV play and pause commands to the AVD  12 , or it may be a more sophisticated device such as a tablet computer, a game controller communicating via wired or wireless link with a game console implemented by another one of the devices shown in  FIG. 1  and controlling video game presentation on the AVD  12 , a personal computer, a wireless telephone, etc. 
     Accordingly, the first CE device  44  may include one or more displays  50  that may be touch-enabled for receiving user input signals via touches on the display  50 . Additionally, or alternatively, the display(s)  50  may be an at least partially transparent display such as an AR headset display or a “smart” glasses display or “heads up” display, as well as a VR headset display, or other display configured for presenting AR and/or VR images. 
     The first CE device  44  may also include one or more speakers  52  for outputting audio in accordance with present principles, and at least one additional input device  54  such as, for example, an audio receiver/microphone for entering audible commands to the first CE device  44  to control the device  44 . The example first CE device  44  may further include one or more network interfaces  56  for communication over the network  22  under control of one or more CE device processors  58 . Thus, the interface  56  may be, without limitation, a Wi-Fi transceiver, which is an example of a wireless computer network interface, including mesh network interfaces. It is to be understood that the processor  58  controls the first CE device  44  to undertake present principles, including the other elements of the first CE device  44  described herein such as, e.g., controlling the display  50  to present images thereon and receiving input therefrom. Furthermore, note that the network interface  56  may be, for example, a wired or wireless modem or router, or other appropriate interface such as a wireless telephony transceiver, or Wi-Fi transceiver as mentioned above, etc. 
     Still further, note that in addition to the processor(s)  58 , the first CE device  44  may also include a graphics processing unit (GPU)  55  on a graphics card  55 A. The graphics processing unit  55  may be configured for, among other things, presenting AR and/or VR images on the display  50 . 
     In addition to the foregoing, the first CE device  44  may also include one or more input ports  60  such as, e.g., a HDMI port or a USB port to physically connect (e.g., using a wired connection) to another CE device and/or a headphone port to connect headphones to the first CE device  44  for presentation of audio from the first CE device  44  to a user through the headphones. The first CE device  44  may further include one or more tangible computer readable storage medium  62  such as disk-based or solid-state storage. Also in some embodiments, the first CE device  44  can include a position or location receiver such as but not limited to a cellphone and/or GPS receiver and/or altimeter  64  that is configured to, e.g., receive geographic position information from at least one satellite and/or cell tower, using triangulation, and provide the information to the CE device processor  58  and/or determine an altitude at which the first CE device  44  is disposed in conjunction with the CE device processor  58 . However, it is to be understood that that another suitable position receiver other than a cellphone and/or GPS receiver and/or altimeter may be used in accordance with present principles to, e.g., determine the location of the first CE device  44  in all three dimensions. 
     Continuing the description of the first CE device  44 , in some embodiments the first CE device  44  may include one or more cameras  66  that may be, e.g., a thermal imaging camera, an IR camera, a digital camera such as a webcam, and/or another type of camera integrated into the first CE device  44  and controllable by the CE device processor  58  to generate pictures/images and/or video in accordance with present principles. Also included on the first CE device  44  may be a Bluetooth transceiver  68  and other Near Field Communication (NFC) element  70  for communication with other devices using Bluetooth and/or NFC technology, respectively. An example NFC element can be a radio frequency identification (RFID) element. 
     Further still, the first CE device  44  may include one or more auxiliary sensors  72  (e.g., a motion sensor such as an accelerometer, gyroscope, cyclometer, or a magnetic sensor, an infrared (IR) sensor, an optical sensor, a speed and/or cadence sensor, a gesture sensor (e.g., for sensing gesture command), etc.) providing input to the CE device processor  58 . The first CE device  44  may include still other sensors such as, for example, one or more climate sensors  74  (e.g., barometers, humidity sensors, wind sensors, light sensors, temperature sensors, etc.) and/or one or more biometric sensors  76  providing input to the CE device processor  58 . In addition to the foregoing, it is noted that in some embodiments the first CE device  44  may also include an infrared (IR) transmitter and/or IR receiver and/or IR transceiver  78  such as an IR data association (IRDA) device. A battery (not shown) may be provided for powering the first CE device  44 . The CE device  44  may communicate with the AVD  12  through any of the above-described communication modes and related components. 
     The second CE device  46  may include some or all of the components shown for the CE device  44 . Either one or both CE devices may be powered by one or more batteries. 
     Now in reference to the afore-mentioned at least one server  80 , it includes at least one server processor  82 , at least one tangible computer readable storage medium  84  such as disk-based or solid-state storage. In an implementation, the medium  84  includes one or more solid state storage drives (SSDs). The server also includes at least one network interface  86  that allows for communication with the other devices of  FIG. 1  over the network  22 , and indeed may facilitate communication between servers and client devices in accordance with present principles. Note that the network interface  86  may be, e.g., a wired or wireless modem or router, Wi-Fi transceiver, or other appropriate interface such as a wireless telephony transceiver. The network interface  86  may be a remote direct memory access (RDMA) interface that directly connects the medium  84  to a network such as a so-called “fabric” without passing through the server processor  82 . The network may include an Ethernet network and/or fiber channel network and/or InfiniBand network. Typically, the server  80  includes multiple processors in multiple computers referred to as “blades” that may be arranged in a physical server “stack”. 
     Accordingly, in some embodiments the server  80  may be an Internet server or an entire “server farm”, and may include and perform “cloud” functions such that the devices of the system  10  may access a “cloud” environment via the server  80  in example embodiments for, e.g., domain adaptation as disclosed herein. Additionally, or alternatively, the server  80  may be implemented by one or more game consoles or other computers in the same room as the other devices shown in  FIG. 1  or nearby. 
       FIGS. 2 and 3  illustrate overall principles. Commencing at block  200  in  FIG. 2 , sound effects (SFX) are classified. In an example, this classification may be executed on incoming digitized sound effect signals  300  to render tags  302  (graphically shown in  FIG. 3 ) that describe in words the sound effects being classified as set forth elsewhere herein. 
     Moving to block  202  in  FIG. 2  and still cross-referencing  FIG. 3 , the tags  302  are registered in a database  304 . Then, proceeding to block  204  in  FIG. 2 , the registered tags may be combined with video without sound  306  to render video with sound effect sound  308 . Note that “sound effects” refer to non-verbal audio that is part of computer simulations such as computer games to mimic the sounds of gunfire, fire burning, people running, people yelling exclamations, water, etc. As set forth further below, deep learning/AI techniques are provided herein to assist in sound content creation for computer simulations such as video games. 
     As used herein, “clean SFX tagging” refers to classifying or tagging clean audio samples (sound effects with a single source of sound) used by game sound designers based on their categories and subcategories, so that they can be registered in a database automatically. This assists the game designers by making search and retrieval during sound mixing more efficient. “Video tagging” refers to recommending sound effects that are relevant to a game scene automatically. This is done to assist game designers by making the sound design process more efficient. Present principles focus on techniques to achieve video tagging. 
     This disclosure divulges two techniques for video tagging.  FIGS. 4-9  describe a direct mapping approach in which a deep learning engine is trained to learn a correlation between the visual features of a game video and corresponding SFX (audio) tags  302 .  FIGS. 10-12  describe a visual understanding approach in two steps, namely, providing a neural network (NN) to understand the visual content of the game scene and generate visual tags, which includes object tags, action tags, and captions, followed by mapping the visual tags to audio tags using semantic text similarity. Dictionary-based mapping may also be used based on other knowledge bases. 
     Accordingly, and now referring to  FIG. 4 , in a training phase  400  video such as computer simulations with SFX sounds  402  are used to train a NN system to generate tags  404  for different SFX sources to render SFX tags  406 . Once the NN system is trained, it may be used in a test phase  408  to receive video  410  such as computer simulations without SFX sounds as input to a trained model  412  described further below to output SFX tags  414  that are combined with the video  410  to render video  416  with SFX sound incorporated therein. 
     Now referring to  FIG. 5 , a more detailed explanation of the training phase from  FIG. 4  may be seen. Silent video such as computer game video  500  is input to a trained NN  502 . A supervised learning approach is used by the NN  502  for learning a direct mapping between visual features of a video and corresponding sound effects. To train this supervised model, sound annotations for the game audio are required. As understood herein, the process is complicated by the fact that game audio typically contains a mixture of sounds (also referred to as noisy SFX), making it difficult to obtain human annotations  504 , especially if number of sound categories is large. Hence, a deep learning model  506  is trained to automatically tag a mixture of sounds (noisy SFX model) to identify the categories of the constituent sounds. 
     Now referring to  FIGS. 6 and 7 , in an initial embodiment a noisy SFX model is trained to tag a small number of categories ( 32  classes) using human annotations. An audio clip represented by the spectrogram  600  is input to a segmentation mapping module  602  that includes a series of convolutional NNs (CNNs)  604 . Segmentation masks  606  are output by the mapping module  602  and used for classification mapping  608  that produces predictions  610  for tags with corresponding probabilities.  FIG. 7  relatedly shows a gated convolutional recurrent NN (CRNN)  700  that receives SFX clips  702  as input and extracts spectral patterns at each time step, providing output to a bidirectional RNN  704  such as a bidirectional long short-term memory (LSTM).  FIG. 7  indicates the types of CNNs used in the network  700 . The Bi-RNN  704  is coupled to an attention-based localization module  706  that includes plural feed forward NNs (FNN) operating as sigmoid and SoftMax FNN as shown to produce predicted tags  708  as weighted averages. 
     Of importance to present principles is  FIG. 8 , illustrating an advanced technique for noisy SFX tagging. To generate finer-grained SFX tags (e.g., 182-class or 5000-class labels or even more detailed) for better discrimination of different sound effects in a noisy sample, a supervised model is trained using actual SFX data  800  from computer simulations and synthesized noisy SFX data  802  generated separately from any simulation solely for purposes of training a gated CNN module  804 . In other words, present principles as reflected in  FIG. 8  recognize that to train a supervised model, training data is required that has finer-grained (e.g., 182-class or 5000 class) ground truth tags, whereas only coarser-grained (32-class) human annotated SFX labels for game audio currently is available. Hence,  FIG. 8  and following figures illustrate a semi-supervised approach that generates fine-grained audio tags from coarse-grained audio tags without additional human annotations. Note that 32-class and 182-class are used as examples of coarse and finer-grained tags. 
     The synthetic mixtures of sound samples represented at  802  are created and their categories recorded during mixing. In this synthetic mixture, fine-grained SFX labels (referred to elsewhere herein as Dataset 1 ) are established. Block  800 , on the other hand, represents the available real game audio with coarse-grained labels (generated by humans) referred to as Dataset 2 . As shown in  FIG. 8 , Dataset 1  of actual simulation or game data and Dataset 2  of synthesized data that is not from a simulation or game but is created for purposes of supplementing game data are combined to train an end-to-end semi-supervised model  804  that includes a coarse classifier  806  and a fine-grained classifier  808  to generate fine-grained tags  810  that identify the components of noisy game audio. It is semi-supervised because no true fine-grained game audio labels are present for training, as explained earlier. It is a multi-tasking model because it is capable of generating both coarse-grained audio tags  812  and fine-grained audio tags  810 . In other words, fine grain analysis uses more categories than coarse grain analysis. 
     The training loss function for this model is a sum of the loss for fine-grained tagging and coarse-grained tagging. The goal of the training is to minimize the training loss. The training stops when the model converges. At this point a model is attained that can decompose a noisy audio mixture into its constituent tags. 
     Accordingly, the above description divulges a technique to identify the constituent sound effect categories of a game audio, while  FIG. 9  depicts how to use these tags (generated by human or by the model in  FIG. 8 ) to train a supervised video tagging model. As shown, during training, videos  900  with sound extracted, along with the noisy SFX tags  902  generated as described above and/or human-annotated, are input to a training phase module  904 . With greater specificity, the corresponding audio that is extracted from the video is passed through the noisy SFX model explained above in  FIG. 8  to generate the SFX tags or labels  902 , which are input along with the corresponding video segment  900  to the supervised training phase model  904 . In this way the video is synchronized with the audio tags before training. In an example non-limiting implementation, the frame rate used may be thirty frames per second (30 fps) and video duration may be one second. 
     The training phase module  904  generates video embeddings (numerical vectors) by passing the silent video frames through a deep CNN  906  (e.g., a Resnet or similar network). For each frame, one embedding (vector) is generated, which serves as the visual feature for the video frame. Other visual features can also be used. Because a video is a sequence of frames, a sequence of video embeddings is produced, which are then input to a recurrent neural network  908 , in the example shown, a bidirectional gated recurrent unit (GRU) or gated recurrent network that produces tag predictions  910 . 
     The output of the training is a neural model  912  that can receive new simulation video  914  without sound in a test phase and generate sound tags  916  corresponding to the silent video  914 . These tags may be used to retrieve the corresponding sound effects  918  for combination with the video as shown at  920 . 
       FIGS. 10-12  illustrate the visual understanding approach alluded to above. In a first step, video  1000  such as a computer simulation without sound (audio) is used to generate visual tags  1002  based on visual understanding of, for example, identified objects  1004  in the video, identified actions  1006  in the video, and identified scene descriptions  1008  in the video. Then a semantic text similarity module  1010  receives the visual tags  1002  along with SFX tags  1012  from the database described above to automatically map the visual tags to the specific audio categories in the sound database to generate video  1014  with sound. 
       FIG. 11  illustrates further. A display  1100  is shown presenting video with objects  1102  that are recognized using image recognition techniques to generate corresponding visual tags  1104 . The visual tags  1104  may be embedded using word embedding or sentence embedding, which results in a numerical vector. The video tags  1104  are matched with corresponding audio tags  1106 . Each audio category or audio file name that identifies an audio sample is embedded using word embedding or sentence embedding, which again results in a numerical vector.  FIG. 12  similarly shows a video on a display  1200  with captions  1202  that can be matched using unsupervised semantic text similarity models  1203  to audio tags  1204 . 
     In any case, whether matching the audio tags to object tags, caption tags, or action tags, two numerical vectors are produced, one for the audio tag and one for the tag derived from the video. The similarity of the tags is determined by computing the distance between the two vectors. Any distance measure, such as cosine similarity or Euclidean distance, can be used. The smaller the distance, the more similar the tags are. Using this approach, each visual tag is mapped to the top-k most similar audio tags. 
     Using this text similarity approach, each visual tag can be mapped to different granularities of audio tags, ranging from coarse grained (e.g., 32-class) tags that identify a group of audio samples to very fine-grained tags that identify an individual sound sample. 
     The automatically generated audio tags from visual understanding of game scenes can serve two purposes. First, the audio tags can be used to recommend sound effects for game scenes to the game designers. Second, the audio tags can also be used as SFX labels for training the direct mapping video tagging model divulged in  FIGS. 4-9  as an alternative to the noisy SFX labels derived from audio. 
     While direct mapping in  FIGS. 4-9  may provide greater accuracy in tagging than the visual understanding technique shown in  FIGS. 10-12 , visual understanding renders finer grained tagging using unsupervised text similarity and renders it relatively easy to annotate objects and captions. Direct mapping is particularly advantageous when accurate ground-truth SFX tags are available for tagging or sound source separation is viable. Visual understanding is particularly advantageous when obtaining fine-grained SFX annotations is otherwise difficult, and it mimics the work flow of a sound engineer. 
     Present principles may be used in deep learning-based methods for image, video and audio data processing, among others. As may be appreciated from the foregoing detailed description, present principles thus improve the adaptation and training of neural networks through the technological solutions described herein. 
     It will be appreciated that whilst present principals have been described with reference to some example embodiments, these are not intended to be limiting, and that various alternative arrangements may be used to implement the subject matter claimed herein.