System and method for automatically extracting and aligning tags derived from camera feed for moving sound sources recorded with microphone array

Abstract

A method for tagging audio data includes receiving video stream data and audio stream data corresponding to at least a portion of the video stream data. The method also includes tagging at least some objects of the video stream data. The method further includes calculating at least one offset value for at least a portion of the audio stream data corresponding to the at least one tagged object of the video stream data. The method further includes synchronizing at least a portion of the video stream data with the portion of the audio stream data. The method further includes tagging at least a portion of the audio stream data corresponding to the at least one tagged object of the video stream data, and generating training data using at least some of the tagged portions of the audio stream data.

Description

Technical Field

[0001] The present disclosure relates to computer systems with artificial intelligence capabilities, including neural networks. In an embodiment, the present disclosure relates to the automatic extraction and alignment of tags derived from video data for moving sound sources recorded with an audio capture array. Background Art

[0002] When developing data for training machine learning models, data collection and labeling, especially sound data, is a laborious, expensive, and time-consuming task that probably represents the main bottleneck in most current machine learning pipelines. Humans use a variety of sound cues from the environment in their daily life decision making. Increasingly, developers are trying to incorporate this decision making into various machine learning models. While the technology of using machine learning models to understand human speech is relatively common, using machine learning models to understand non-speech environmental sounds is a relatively young field and a rapidly growing topic of interest. Summary of the invention

[0003] One aspect of the disclosed embodiment includes a method for generating training data. The method includes receiving audio stream data associated with at least one audio capture array disposed in a data capture environment, receiving video stream data associated with the data capture environment simultaneously with the audio stream data, the video stream data being associated with at least one image capture array disposed in the data capture environment at a distance from the at least one audio capture array, and identifying one or more objects in the video stream data using output from at least a first machine learning model, the first machine learning model being configured to provide output including one or more object detection predictions. The method also includes, for at least one of the one or more objects identified in the video stream data, using output from a probability-based audio event detector to identify a portion of the audio stream data corresponding to the at least one object. The method also includes calculating at least one offset value for at least a portion of the audio stream data corresponding to at least one marked object of the video stream data based on at least one data capture characteristic, and for the at least one object, calculating a shift value based on a cross-correlation between a portion of the video stream data corresponding to the at least one object and a portion of the audio stream data corresponding to the at least one object. The method also includes synchronizing a portion of the video stream data corresponding to the at least one object with a portion of the audio stream data corresponding to the at least one object using at least one offset value and a shift value, and marking at least a portion of the audio stream data corresponding to the at least one object using one or more tags extracted for the corresponding object of the video stream data, the at least one offset value, and the shift value. The method also includes generating training data using at least one of the marked portions of the audio stream data.

[0004] Another aspect of the disclosed embodiment includes a system for training an audio recognition machine learning model. The system includes a processor and a memory. The memory includes instructions that, when executed by the processor, cause the processor to: receive audio stream data associated with at least one audio capture array disposed in a data capture environment, receive video stream data associated with the data capture environment simultaneously with the audio stream data, the video stream data being associated with at least one image capture array disposed in the data capture environment at a distance from the at least one audio capture array; use output from at least a first machine learning model to identify one or more objects in the video stream data, the first machine learning model being configured to provide output including one or more object detection predictions; for at least one of the one or more objects identified in the video stream data, use output from a probability-based audio event detector to identify a portion of the audio stream data corresponding to the at least one object; based on at least one Data capture characteristics, calculating at least one offset value for at least a portion of audio stream data corresponding to at least one marked object of video stream data; for the at least one object, calculating a shift value based on a cross-correlation between a portion of the video stream data corresponding to the at least one object and a portion of the audio stream data corresponding to the at least one object; synchronizing a portion of the video stream data corresponding to the at least one object with a portion of the audio stream data corresponding to the at least one object using at least at least one offset value and a shift value; marking at least a portion of the audio stream data corresponding to the at least one object using one or more tags extracted for the corresponding object of the video stream data, at least one offset value, and a shift value; generating training data using at least one of the marked portions of the audio stream data; and training an audio recognition machine learning model using the training data.

[0005] Another aspect of the disclosed embodiment includes an apparatus for training an audio recognition machine learning model. The apparatus includes a processor and a memory. The memory includes instructions that, when executed by the processor, cause the processor to: receive audio stream data associated with at least one audio capture device disposed in a data capture environment; receive video stream data associated with the data capture environment simultaneously with the audio stream data, the video stream data being associated with at least one image capture device disposed in the data capture environment at a distance from the at least one audio capture device; use output from at least a first machine learning model to identify one or more objects in the video stream data, the first machine learning model being configured to provide output including one or more object detection predictions; for at least one of the one or more objects identified in the video stream data, use output from a probability-based audio event detector to identify a portion of the audio stream data corresponding to the at least one object; and calculate, based on at least one data capture characteristic, an audio corresponding to at least one labeled object in the video stream data. at least one offset value for at least a portion of streaming data; for the at least one object, calculating a shift value based on a cross-correlation between a portion of video stream data corresponding to the at least one object and a portion of audio stream data corresponding to the at least one object; synchronizing the portion of video stream data corresponding to the at least one object with a portion of audio stream data corresponding to the at least one object using at least at least one offset value and a shift value; marking at least a portion of the audio stream data corresponding to the at least one object using one or more tags extracted for the corresponding object of the video stream data, at least one offset value, and a shift value; generating training data using at least one of the marked portions of the audio stream data; and training an audio recognition machine learning model using the training data, the audio recognition machine learning model that has been trained using the training data being configured to detect one or more events in the audio stream data provided as input to the audio recognition machine learning model. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1 A system for training a neural network according to the principles of the present disclosure is generally illustrated.

[0007] Figure 2 A computer-implemented method for training and utilizing a neural network in accordance with the principles of the present disclosure is generally illustrated.

[0008] Figure 3A-3B An audio data tagging system in accordance with the principles of the present disclosure is generally illustrated.

[0009] Figure 4 is a flow chart generally illustrating a method of audio data labeling according to the principles of the present disclosure.

[0010] Figure 5Depicted is a schematic diagram of the interaction between a computer-controlled machine and a control system in accordance with the principles of the present disclosure.

[0011] Figure 6 Depicted is a device configured to control a vehicle according to the principles of the present disclosure. Figure 5 Schematic diagram of a control system of a vehicle, which can be a partially autonomous vehicle, a fully autonomous vehicle, a partially autonomous robot, or a fully autonomous robot.

[0012] Figure 7 Depicted Figure 5 Schematic diagram of a control system configured to control a manufacturing machine, such as a punch, a cutting machine, or a gun drill, of a manufacturing system (such as part of a production line).

[0013] Figure 8 Depicted Figure 5 Schematic diagram of a control system configured to control a power tool, such as a drill or driver, having an at least partially autonomous mode.

[0014] Fig. 9 Depicting a device configured to control an automated personal assistant Figure 5 Schematic diagram of the control system.

[0015] Fig.10 Depicted Figure 5 Schematic diagram of a control system configured to control a surveillance system, such as a controlled access system or a monitoring system.

[0016] Fig.11 Depicted Figure 5 Schematic diagram of a control system configured to control an imaging system, such as an MRI device, an x-ray imaging device, or an ultrasound device. DETAILED DESCRIPTION

[0017] Embodiments of the present disclosure are described herein. However, it should be understood that the disclosed embodiments are merely examples, and other embodiments may take various and alternative forms. The figures are not necessarily to scale; some features may be enlarged or reduced to show the details of a particular component. Therefore, the specific structural and functional details disclosed herein should not be interpreted as restrictive, but merely as a representative basis for teaching those skilled in the art to adopt the embodiments in various ways. As will be understood by those of ordinary skill in the art, the various features illustrated and described with reference to any of the figures may be combined with the features illustrated in one or more other figures to produce embodiments that are not explicitly illustrated or described. The combination of the illustrated features provides representative embodiments of typical applications. However, for specific applications or implementations, various combinations and modifications of features consistent with the teachings of the present disclosure may be desired.

[0018] As mentioned, data collection and labeling, especially sound data, is a laborious, expensive, and time-consuming task when developing data for training machine learning models, which may represent a major bottleneck in most current machine learning pipelines. Humans use a variety of sound cues from the environment in daily life decision making. Increasingly, developers are trying to incorporate this decision making into various machine learning models. While the technology of using machine learning models to understand human speech is relatively common, using machine learning models to understand non-speech environmental sounds is a relatively young field and a rapidly growing topic of interest.

[0019] Recent advances in vision and text deep learning techniques come from access to large amounts of labeled data. However, collecting and strongly labeling audio data (e.g., including marking the types of events in an audio sample and the start and end of an event) can be relatively difficult. Audio data collection is often a challenging task due to the essentially infinite vocabulary of audio in real-world scenarios, which can make predicting the vocabulary for a given task essentially impossible.

[0020] Unlike spoken words, which use a finite set of alphabets, the variations of environmental sounds are infinite. As a result, it is unrealistic to collect specific sounds in a preset environment. Therefore, most audio data collection is captured in a continuous setting, and later, human annotators extract and mark the desired audio events. Such labeling can be relatively time-consuming (e.g., because annotators may listen to hours of data to find events of interest or target events). In addition, after finding the target event, accurately marking the start and end of the audio event can be noisy and subjective due to the transient nature of the signal, which often lacks clear boundaries.

[0021] Another common way to collect audio data is to use available data (e.g., from various public sources). However, such data is often of low quality, and the associated labels for such data can often be automatically created using the titles of the audio or video files associated with the data. These labels are sometimes noisy and are often weak labels (e.g., a portion of an audio file is labeled as an audio event, but the exact temporal boundaries of the event are not specified). This often creates many challenges for traditional machine learning and deep learning model training.

[0022] Therefore, systems and methods (such as those described herein) configured to pair audio data collection with video streams and combine advances in the visual field to automatically tag audio data may be desirable. In some embodiments, the systems and methods described herein can be configured to collect and annotate mobile sound sources using an audio capture array, such as a microphone array or other suitable audio capture array or device.

[0023] In some embodiments, the systems and methods described herein can be configured to collect data using a tandem video capture device (e.g., a camera or other suitable image capture device) and an audio capture array. The systems and methods described herein can be configured to automatically tag mobile sources that appear in video data. The systems and methods described herein can be configured to synchronize the collected audio and video data in conjunction with a signal processing-based method. The systems and methods described herein can be configured to extract corresponding audio tags. The systems and methods described herein can be configured to use any suitable combination of microphone arrays and other sensors, which will provide automatic tagging for mobile sound sources.

[0024] In some embodiments, the systems and methods described herein can be configured to collect both audio and video stream data from a data capture or collection environment. The systems and methods described herein can be configured to automatically mark and track objects in video stream data using a pre-trained object recognition model and mark the corresponding audio data.

[0025] The systems and methods described herein can be configured to identify misalignments between audio and video stream data based on the positioning of the image capture device relative to the audio capture array and based on the field of view of the image capture device. The systems and methods described herein can be configured to map video tags to audio data based on a time shift between the image capture device and the audio capture array. In some embodiments, the systems and methods described herein can be configured to manually observe one or more samples of audio data and video data to identify a time shift (e.g., assuming that the time shift is constant).

[0026] Additionally or alternatively, in response to a non-constant time shift between the image capture device and the audio capture array (e.g., due to image capture device viewpoint, speed of the object, direction of travel of the object, drift between asynchronous audio and video collection systems, etc.), the systems and methods described herein can be configured to automatically align object labels extracted from the video stream data with the audio stream data using a probability-based approach.

[0027] The system and method described herein can be configured to mark mobile sound events accompanied by visual cues / actions. The system and method described herein can be configured to extract the direction of a moving object from video stream data. The system and method described herein can be configured to use the extracted direction of a moving object as a label for audio event positioning and direction of arrival estimation (e.g., the task of detecting the position of a sound and the relative direction of a sound). In some embodiments, the system and method described herein can be configured to provide an initial tag prediction and receive further annotations from a user.

[0028] As described, the systems and methods described herein can be configured to automatically align object tags extracted from video stream data with audio stream data using a probability-based approach. Since image capture devices and audio capture devices typically do not share the same clock (e.g., two independent devices), the systems and methods described herein can be configured to perform synchronization between object tags extracted from video stream data and sound events recorded by an audio capture array. When the field of view of the image capture device causes the maximum sound intensity of a particular event to occur when the object is not in the center of the video frame, this synchronization may be particularly challenging because objects moving in different directions present different offsets between tags extracted from the video stream data and sound events recorded in the audio stream data.

[0029] In some embodiments, given a list of objects automatically detected from video stream data, where each object v = (t s ,t e ,d,l) by the start time t s , end time t e , label l and direction d (from left to right, from right to left), for each object, the systems and methods described herein can be configured to calculate the center time as follows:

[0030]

[0031] The systems and methods described herein may be configured to load y a (t),t∈[t c -δ,t c +δ], that is, t c -δ and t c +δ, where δ is a margin large enough so that the sound of a particular object is audible.

[0032] The systems and methods described herein may be configured to calculate L a (t,l,d) — an object with label l and direction d at time y a (t). For example, the systems and methods described herein can be configured to a When (t) is the signal set from the audio capture array, the likelihood function L is calculated by a (t,l,d): for y a (t) filtering to retain only the bandwidth of interest for a particular type of sound event l; from y a (t); and for each window, compute the response of the beamforming algorithm steered to a predefined set of angles. Given N a Angles and N ttime windows, which results in a Direction of Arrival (DoA) graph where each element D(i,j),i∈[1,N a ],j∈[1,N t ] represents the energy from the i-th angle in the j-th time window.

[0033] The systems and methods described herein can be configured to execute a beamforming algorithm that receives as input the signals recorded by the audio capture array and the steering direction. The systems and methods described herein can be configured to calculate the acoustic energy associated with such a direction. For example, a beamforming algorithm that steers toward α (e.g., the angle between the audio capture array 304 and a first object (e.g., the first vehicle 308)) or β (e.g., the angle between the audio capture array 304 and a second object (e.g., the second vehicle 310)) will exhibit higher energy than when steered toward other directions, such as Figure 3B As shown in the figure.

[0034] The systems and methods described herein may be configured to select a predefined DoA template (e.g., a matrix Where N p is the number of time windows spanned by the template). Each element P(i,j),i∈[1,N a ],j∈[1,N p ] represents the energy from the i-th angle in the j-th time window. It is derived from the 2D cross-correlation (pattern matching) between D and P, which can be defined as:

[0035]

[0036] The systems and methods described herein can be configured to provide pattern matching, where a minimal family of patterns P includes two patterns, one for a sound object traveling from left to right and one for a sound object traveling from right to left. The systems and methods described herein can be configured to determine the time t p , where L a (t,l,d) is the maximum value. The offset between the video object and the corresponding sound event is t p -t c .

[0037] In some embodiments, the systems and methods described herein can be configured to combine a video object detection model in video stream data to automatically mark corresponding moving sound sources that accompany visual cues / actions. The systems and methods described herein can be configured to extract trajectories of moving sound sources in combination with a video object detection model. The systems and methods described herein can be configured to automatically synchronize isolated moving sound events with corresponding labels from a video object detection model using a probability-based approach. The systems and methods described herein can be configured to use any other sensor for automatically marking moving sound sources. The systems and methods described herein can be configured to provide data synchronization to align labels generated with different sensors.

[0038] In some embodiments, the systems and methods described herein can be configured to receive video stream data associated with a data capture environment from at least one image capture device (e.g., such as a camera or other suitable image capture device). It should be understood that the systems and methods described herein can be configured to use any suitable number of image capture devices.

[0039] The systems and methods described herein can be configured to receive audio stream data corresponding to at least a portion of the video stream data from at least one audio capture array (e.g., such as one or more microphones or one or more other suitable audio capture arrays or devices). The at least one audio capture array is positioned away from the at least one image capture device, or positioned near the at least one image capture device. The systems and methods described herein can be configured to label at least some objects of the video stream data using output from at least a first machine learning model, the first machine learning model being configured to provide output including one or more object detection predictions.

[0040] The systems and methods described herein may be configured to calculate (e.g., using at least one probability-based function or other suitable technique or function) at least one offset value for at least a portion of the audio stream data corresponding to at least one marked object of the video stream data based on at least one data capture characteristic. The systems and methods described herein may be configured to synchronize at least a portion of the video stream data with a portion of the audio stream data corresponding to at least one marked object of the video stream data (e.g., align a video object of the video stream data with a corresponding audio event of the audio stream data) using at least the at least one offset value. The at least one data capture characteristic may include one or more characteristics of at least one image capture device, one or more characteristics of at least one audio capture array, one or more characteristics corresponding to a position of at least one image capture device relative to at least one audio capture array, one or more characteristics corresponding to movement of an object in the video stream data, one or more other suitable data capture characteristics, or a combination thereof.

[0041] The systems and methods described herein may be configured to mark at least a portion of the audio stream data corresponding to at least one marked object of the video stream data using one or more tags and at least one offset value of the marked object of the video stream data. Each corresponding tag may include an event type, an event start indicator, and an event end indicator. The systems and methods described herein may be configured to generate training data using at least some of the marked portions of the audio stream data. The systems and methods described herein may be configured to train a second machine learning model using the training data. The systems and methods described herein may be configured to use the second machine learning model to detect one or more sounds associated with audio data provided as input to the second machine learning model.

[0042] Figure 1 A system 100 for training a neural network is shown. The system 100 may include an input interface for accessing training data 102 for the neural network. For example, Figure 1 As shown in FIG. 1 , the input interface may be constituted by a data storage interface 104, which may access the training data 102 from a data storage device 106. For example, the data storage interface 104 may be a memory interface or a permanent storage interface, such as a hard disk or SSD interface, but may also be a personal, local area network or wide area network interface, such as a Bluetooth, Zigbee or Wi-Fi interface or an Ethernet or fiber optic interface. The data storage device 106 may be an internal data storage device of the system 100, such as a hard disk drive or SSD, but may also be an external data storage device, such as a network accessible data storage device.

[0043] In some embodiments, data storage device 106 may also include a data representation 108 of an untrained version of the neural network, which may be accessed by system 100 from data storage device 106. However, it will be appreciated that training data 102 and data representation 108 of the untrained neural network may also each be accessed from a different data storage device, such as via a different subsystem of data storage interface 104. Each subsystem may be of a type as described above with respect to data storage interface 104.

[0044] In some embodiments, the data representation 108 of the untrained neural network may be generated internally by the system 100 based on the design parameters of the neural network, and thus may not be explicitly stored on the data storage device 106. The system 100 may also include a processor subsystem 110 that may be configured to provide an iterated function as a replacement for a stack of layers of the neural network to be trained during operation of the system 100. Here, the layers of the replaced stack may have mutually shared weights and may receive as input the output of a previous layer, or, for a first layer of the stack, receive an initial activation and a portion of the input of the stack.

[0045] The processor subsystem 110 may also be configured to iteratively train the neural network using the training data 102. Here, the training iterations of the processor subsystem 110 may include a forward propagation portion and a backward propagation portion. The processor subsystem 110 may be configured to perform the forward propagation portion by, among other operations that may be performed to define the forward propagation portion, determining an equilibrium point of the iteration function at which the iteration function converges to a fixed point, wherein determining the equilibrium point includes using a numerical root finding algorithm to find a root solution of the iteration function minus its input, and by providing the equilibrium point as a surrogate for the output of the stack of layers in the neural network.

[0046] The system 100 may also include an output interface for outputting a data representation 112 of the trained neural network, which may also be referred to as trained model data 112. Figure 1 , the output interface may be comprised of a data storage interface 104, wherein in these embodiments, the interface is an input / output ("IO") interface via which trained model data 112 may be stored in a data storage device 106. For example, a data representation 108 defining an "untrained" neural network may be at least partially replaced by a data representation 112 of a trained neural network during or after training, since the parameters of the neural network (such as weights, hyperparameters, and other types of parameters of the neural network) may be adapted to reflect the training on the training data 102. This is also Figure 1 106, which are illustrated by reference numerals 108, 112, which refer to the same data record on the data storage device 106. In some embodiments, the data representation 112 may be stored separately from the data representation 108 defining the "untrained" neural network. In some embodiments, the output interface may be separate from the data storage interface 104, but may generally be of the type described above for the data storage interface 104.

[0047] Figure 2 A data annotation / augmentation system 200 implementing a system for annotating and / or augmenting data is depicted. The data annotation system 200 may include at least one computing system 202. The computing system 202 may include at least one processor 204 operatively connected to a memory unit 208. The processor 204 may include one or more integrated circuits implementing the functionality of a central processing unit (CPU) 206. The CPU 206 may be a commercially available processing unit that implements an instruction set such as one of the x86, ARM, Power, or MIPS instruction set families.

[0048] During operation, CPU 206 may execute stored program instructions retrieved from memory unit 208. The stored program instructions may include software that controls the operation of CPU 206 to perform the operations described herein. In some embodiments, processor 204 may be a system on a chip (SoC) that integrates the functionality of CPU 206, memory unit 208, network interfaces, and input / output interfaces into a single integrated device. Computing system 202 may implement an operating system for managing various aspects of operation.

[0049] Memory unit 208 may include volatile memory and non-volatile memory for storing instructions and data. Non-volatile memory may include solid-state memory, such as NAND flash memory, magnetic and optical storage media, or any other suitable data storage device that retains data when computing system 202 is deactivated or powered off. Volatile memory may include static and dynamic random access memory (RAM) that stores program instructions and data. For example, memory unit 208 may store machine learning model 210 (e.g., in Figure 2 210), or an algorithm, a training data set 212 of the machine learning model 210, and an original source data set 216.

[0050] The computing system 202 may include a network interface device 222 configured to provide communications with external systems and devices. For example, the network interface device 222 may include a wired and / or wireless Ethernet interface defined by the Institute of Electrical and Electronics Engineers (IEEE) 802.11 family of standards. The network interface device 222 may include a cellular communication interface for communicating with a cellular network (e.g., 3G, 4G, 5G). The network interface device 222 may also be configured to provide a communication interface to an external network 224 or a cloud.

[0051] The external network 224 may be referred to as the World Wide Web or the Internet. The external network 224 may establish a standard communication protocol between computing devices. The external network 224 may allow information and data to be easily exchanged between computing devices and the network. One or more servers 230 may communicate with the external network 224.

[0052] Computing system 202 may include input / output (I / O) interface 220, which may be configured to provide digital and / or analog input and output. I / O interface 220 may include an additional serial interface (eg, a universal serial bus (USB) interface) for communicating with external devices.

[0053] The computing system 202 may include a human-machine interface (HMI) device 218, which may include any device that enables the system 200 to receive control inputs. Examples of input devices may include human-machine interface inputs, such as keyboards, mice, touch screens, voice input devices, and other similar devices. The computing system 202 may include a display device 232. The computing system 202 may include hardware and software for outputting graphics and text information to the display device 232. The display device 232 may include an electronic display screen, a projector, a printer, or other suitable devices for displaying information to a user or operator. The computing system 202 may also be configured to allow interaction with a remote HMI and a remote display device via a network interface device 222.

[0054] System 200 may be implemented using one or more computing systems. Although the example depicts a single computing system 202 that implements all described features, it is intended that the various features and functions may be separated and implemented by multiple computing units that communicate with each other. The specific system architecture selected may depend on a variety of factors.

[0055] The system 200 may implement a machine learning algorithm 210 configured to analyze a raw source data set 216. The raw source data set 216 may include raw or unprocessed sensor data, which may represent an input data set for the machine learning system. The raw source data set 216 may include videos, video clips, images, text-based information, and raw or partially processed sensor data (e.g., a radar map of an object). In some embodiments, the machine learning algorithm 210 may be a neural network algorithm designed to perform a predetermined function. For example, a neural network algorithm may be configured in an automotive application to identify pedestrians in a video image.

[0056] The computer system 200 may store a training data set 212 for the machine learning algorithm 210. The training data set 212 may represent a previously constructed data set for training the machine learning algorithm 210. The machine learning algorithm 210 may use the training data set 212 to learn weighting factors associated with the neural network algorithm. The training data set 212 may include a source data set with corresponding achievements or results that the machine learning algorithm 210 attempts to replicate via the learning process. In this example, the training data set 212 may include source videos with and without pedestrians and corresponding presence and location information. The source video may include various scenarios in which pedestrians are identified.

[0057] The machine learning algorithm 210 can operate in a learning mode using the training data set 212 as input. The machine learning algorithm 210 can perform multiple iterations using data from the training data set 212. With each iteration, the machine learning algorithm 210 can update internal weighting factors based on the results obtained. For example, the machine learning algorithm 210 can compare the output results (e.g., annotations) with those included in the training data set 212. Since the training data set 212 includes expected results, the machine learning algorithm 210 can determine when the performance is acceptable. After the machine learning algorithm 210 reaches a predetermined performance level (e.g., 100% consistent with the results associated with the training data set 212), the machine learning algorithm 210 can be executed using data that is not in the training data set 212. The trained machine learning algorithm 210 can be applied to a new data set to generate annotated data.

[0058] The machine learning algorithm 210 may be configured to identify specific features in the raw source data 216. The raw source data 216 may include multiple instances or input data sets for which annotation results are desired. For example, the machine learning algorithm 210 may be configured to identify the presence of pedestrians in a video image and annotate the presence. The machine learning algorithm 210 may be programmed to process the raw source data 216 to identify the presence of specific features. The machine learning algorithm 210 may be configured to identify features in the raw source data 216 as predetermined features (e.g., pedestrians). The raw source data 216 may be derived from various sources. For example, the raw source data 216 may be actual input data collected by the machine learning system. The raw source data 216 may be machine generated for testing the system. As an example, the raw source data 216 may include raw video images from a camera.

[0059] In this example, the machine learning algorithm 210 can process the raw source data 216 and output an indication of an image representation. The output can also include an augmented representation of the image. The machine learning algorithm 210 can generate a confidence level or factor for each generated output. For example, a confidence value exceeding a predetermined high confidence threshold can indicate that the machine learning algorithm 210 is confident that the identified feature corresponds to a particular feature. A confidence value less than a low confidence threshold can indicate that the machine learning algorithm 210 has some uncertainty about the existence of a particular feature.

[0060] like Figure 3A and 3B, system 300 may include an image capture device 302, an audio capture array 304, and a computing system 202. System 202 may receive video stream data associated with a data capture environment from image capture device 302. System 202 may be configured to perform video object detection to identify one or more objects in a corresponding image of the video stream data. System 202 may receive audio stream data corresponding to at least a portion of the video stream data from audio capture array 304. Audio capture array 304 may include one or more microphones 306 or other suitable audio capture devices. The systems and methods described herein may be configured to label at least some objects of the video stream data using output from at least a first machine learning model (e.g., such as machine learning model 210 or other suitable machine learning model), the first machine learning model being configured to provide output including one or more object detection predictions.

[0061] The system 202 may calculate (e.g., using at least one probability-based function or other suitable technique or function) at least one offset value for at least a portion of the audio stream data corresponding to at least one marked object of the video stream data based on at least one data capture characteristic. The system 202 may use at least the at least one offset value to synchronize at least a portion of the video stream data with a portion of the audio stream data corresponding to at least one marked object of the video stream data. The at least one data capture characteristic may include one or more characteristics of at least one image capture device, one or more characteristics of at least one audio capture array, one or more characteristics corresponding to a position of at least one image capture device relative to at least one audio capture array, one or more characteristics corresponding to movement of an object in the video stream data, one or more other suitable data capture characteristics, or a combination thereof.

[0062] The system 202 may use one or more tags of the tagged objects of the video stream data and at least one offset value to tag at least a portion of the audio stream data corresponding to at least one tagged object of the video stream data. Each corresponding tag may include an event type, an event start indicator, and an event end indicator. The system 202 may use at least some of the tagged portions of the audio stream data to generate training data. The system 202 may use the training data to train a second machine learning model. The system 202 may use the second machine learning model to detect one or more sounds associated with the audio data provided as input to the second machine learning model. The second machine learning model may include any suitable machine learning model and may be configured to perform any suitable function, such as the one described herein with reference to Figure 6-11 Those features described.

[0063] Figure 44 is a flowchart generally illustrating an audio data tagging method 400 according to the principles of the present disclosure. At 402, the method 400 receives video stream data associated with a data capture environment from at least one image capture device. For example, the system 202 can receive the video stream data from the image capture device 302.

[0064] At 404 , method 400 receives audio stream data corresponding to at least a portion of the video stream data from at least one audio capture array. For example, system 202 may receive audio stream data from audio capture array 304 .

[0065] At 406, method 400 uses output from at least a first machine learning model to label at least some objects of the video stream data, the first machine learning model being configured to provide output including one or more object detection predictions. For example, system 202 can use output from machine learning model 210 to label at least some objects of the video stream data.

[0066] At 408, method 400 calculates at least one offset value for at least a portion of the audio stream data corresponding to at least one marked object of the video stream data based on the at least one data capture characteristic.For example, system 202 can calculate at least one offset value.

[0067] At 410, method 400 uses at least the at least one offset value to synchronize at least a portion of the video stream data with a portion of the audio stream data corresponding to at least one marked object of the video stream data. For example, system 202 may use at least the at least one offset value to synchronize at least a portion of the video stream data with a portion of the audio stream data corresponding to at least one marked object of the video stream data.

[0068] At 412, method 400 uses one or more tags and at least one offset value of the tag object of the video stream data to tag at least a portion of the audio stream data corresponding to the at least one tagged object of the video stream data. For example, system 202 can use one or more tags and at least one offset value of the tag object of the video stream data to tag at least a portion of the audio stream data corresponding to the at least one tagged object of the video stream data.

[0069] At 414, method 400 generates training data using at least some of the marked portions of the audio stream data.For example, system 202 can generate training data using at least some of the marked portions of the audio stream data.

[0070] At 416, method 400 trains a second machine learning model using the training data. For example, system 202 can train a second machine learning model using the training data.

[0071] Figure 5A schematic diagram of the interaction between a computer-controlled machine 500 and a control system 502 is depicted. The computer-controlled machine 500 includes an actuator 504 and a sensor 506. The actuator 504 may include one or more actuators, and the sensor 506 may include one or more sensors. The sensor 506 is configured to sense a condition of the computer-controlled machine 500. The sensor 506 may be configured to encode the sensed condition into a sensor signal 508 and transmit the sensor signal 508 to the control system 502. Non-limiting examples of the sensor 506 include video, radar, LiDAR, ultrasound, and motion sensors. In some embodiments, the sensor 506 is an optical sensor configured to sense an optical image of the environment near the computer-controlled machine 500.

[0072] The control system 502 is configured to receive sensor signals 508 from the computer controlled machine 500. As described below, the control system 502 may be further configured to calculate actuator control commands 510 depending on the sensor signals and transmit the actuator control commands 510 to the actuators 504 of the computer controlled machine 500.

[0073] like Figure 5 As shown in , the control system 502 includes a receiving unit 512. The receiving unit 512 can be configured to receive the sensor signal 508 from the sensor 506 and transform the sensor signal 508 into an input signal x. In an alternative embodiment, the sensor signal 508 is directly received as the input signal x without the receiving unit 512. Each input signal x can be a part of each sensor signal 508. The receiving unit 512 can be configured to process each sensor signal 508 to generate each input signal x. The input signal x can include data corresponding to the image recorded by the sensor 506.

[0074] The control system 502 includes a classifier 514. The classifier 514 can be configured to classify the input signal x into one or more labels using a machine learning (ML) algorithm, such as the neural network described above. The classifier 514 is configured to be parameterized by parameters, such as those described above (e.g., parameter θ). The parameter θ can be stored in and provided by the non-volatile storage device 516. The classifier 514 is configured to determine the output signal y from the input signal x. Each output signal y includes information that assigns one or more labels to each input signal x. The classifier 514 can transmit the output signal y to a conversion unit 518. The conversion unit 518 is configured to convert the output signal y into an actuator control command 510. The control system 502 is configured to transmit the actuator control command 510 to the actuator 504, and the actuator 504 is configured to actuate the computer-controlled machine 500 in response to the actuator control command 510. In some embodiments, actuator 504 is configured to actuate computer-controlled machine 500 directly based on output signal y.

[0075] When the actuator 504 receives the actuator control command 510, the actuator 504 is configured to perform an action corresponding to the associated actuator control command 510. The actuator 504 may include control logic configured to transform the actuator control command 510 into a second actuator control command for controlling the actuator 504. In one or more embodiments, the actuator control command 510 may be used to control a display instead of or in addition to the actuator.

[0076] In some embodiments, the control system 502 includes the sensor 506 instead of or in addition to the computer-controlled machine 500 including the sensor 506. The control system 502 may also include the actuator 504 instead of or in addition to the computer-controlled machine 500 including the actuator 504.

[0077] like Figure 5 As shown in , the control system 502 also includes a processor 520 and a memory 522. The processor 520 may include one or more processors. The memory 522 may include one or more memory devices. The classifier 514 (e.g., ML algorithm) of one or more embodiments may be implemented by the control system 502, which includes a non-volatile storage device 516, a processor 520, and a memory 522.

[0078] The non-volatile storage 516 may include one or more permanent data storage devices, such as a hard drive, an optical drive, a tape drive, a non-volatile solid-state device, a cloud storage device, or any other device capable of permanently storing information. The processor 520 may include one or more devices selected from a high-performance computing (HPC) system, including a high-performance core, a microprocessor, a microcontroller, a digital signal processor, a microcomputer, a central processing unit, a field programmable gate array, a programmable logic device, a state machine, a logic circuit, an analog circuit, a digital circuit, or any other device that manipulates signals (analog or digital) based on computer-executable instructions residing in the memory 522. The memory 522 may include a single memory device or multiple memory devices, including but not limited to random access memory (RAM), volatile memory, non-volatile memory, static random access memory (SRAM), dynamic random access memory (DRAM), flash memory, cache memory, or any other device capable of storing information.

[0079] The processor 520 may be configured to read into the memory 522 and execute computer executable instructions residing in the non-volatile storage device 516, and embody one or more ML algorithms and / or method techniques of one or more embodiments. The non-volatile storage device 516 may include one or more operating systems and applications. The non-volatile storage device 516 may store compiled and / or interpreted from computer programs created using various programming languages ​​and / or technologies, including but not limited to Java, C, C++, C#, Objective C, Fortran, Pascal, JavaScript, Python, Perl, and PL / SQL, alone or in combination.

[0080] When executed by the processor 520, the computer executable instructions of the non-volatile storage device 516 can cause the control system 502 to implement one or more ML algorithms and / or methodological techniques as disclosed herein. The non-volatile storage device 516 can also include ML data (including data parameters) that support the functions, features, and processes of one or more embodiments described herein.

[0081] The program code embodying the algorithm and / or method technology described herein can be distributed separately or jointly as a program product in various forms. The program code can be distributed using a computer-readable storage medium having computer-readable program instructions thereon, for causing the processor to implement aspects of one or more embodiments. The essentially non-temporary computer-readable storage medium may include volatile and non-volatile and removable and non-removable tangible media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules or other data. The computer-readable storage medium may also include RAM, ROM, erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other solid-state storage technology, portable compact disk read-only memory (CD-ROM) or other optical storage device, cassette, tape, magnetic disk storage device or other magnetic storage device, or any other medium that can be used to store the desired information and can be read by a computer. The computer-readable program instructions can be downloaded from the computer-readable storage medium to a computer, another type of programmable data processing device or another device, or downloaded to an external computer or external storage device via a network.

[0082] Computer-readable program instructions stored in a computer-readable medium can be used to guide a computer, other types of programmable data processing devices, or other devices to operate in a particular manner so that the instructions stored in the computer-readable medium produce an article of manufacture including instructions for implementing the functions, actions, and / or operations specified in the flowchart or chart. In certain alternative embodiments, consistent with one or more embodiments, the functions, actions, and / or operations specified in the flowchart and chart can be reordered, processed serially, and / or processed concurrently. In addition, any flowchart and / or chart may include more or fewer nodes or blocks than those illustrated consistent with one or more embodiments.

[0083] The process, method or algorithm may be embodied in whole or in part using suitable hardware components, such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a state machine, a controller or other hardware component or device, or a combination of hardware, software and firmware components.

[0084] Figure 6A schematic diagram of a control system 502 configured to control a vehicle 600, which may be an at least partially autonomous vehicle or an at least partially autonomous robot, is depicted. The vehicle 600 includes an actuator 504 and a sensor 506. The sensor 506 may include one or more video sensors, cameras, radar sensors, ultrasonic sensors, LiDAR sensors, and / or location sensors (e.g., GPS). One or more of the one or more specific sensors may be integrated into the vehicle 600. Alternatively or in addition to the one or more specific sensors described above, the sensor 506 may include a software module that is configured to determine the state of the actuator 504 when executed. A non-limiting example of a software module includes a weather information software module that is configured to determine the current or future state of the weather near the vehicle 600 or other location.

[0085] The classifier 514 of the control system 502 of the vehicle 600 may be configured to detect an object near the vehicle 600 depending on the input signal x. In such an embodiment, the output signal y may include information characterizing the object near the vehicle 600. The actuator control command 510 may be determined based on the information. The actuator control command 510 may be used to avoid a collision with the detected object.

[0086] In some embodiments, the vehicle 600 is an at least partially autonomous vehicle, and the actuator 504 may be embodied in the brakes, propulsion system, engine, drive train, or steering of the vehicle 600. Actuator control commands 510 may be determined to control the actuator 504 so that the vehicle 600 avoids colliding with a detected object. The detected objects may also be classified according to what the classifier 514 thinks they are most likely to be, such as a pedestrian or a tree. The actuator control commands 510 may be determined depending on the classification. In scenarios where adversarial attacks may occur, the above system may be further trained to better detect objects or identify changes in lighting conditions or angles of sensors or cameras on the vehicle 600.

[0087] In some embodiments where the vehicle 600 is an at least partially autonomous robot, the vehicle 600 may be a mobile robot configured to perform one or more functions, such as flying, swimming, diving, and walking. The mobile robot may be an at least partially autonomous lawn mower or an at least partially autonomous cleaning robot. In such embodiments, the actuator control commands 510 may be determined such that a propulsion unit, a steering unit, and / or a braking unit of the mobile robot may be controlled such that the mobile robot may avoid colliding with an identified object.

[0088] In some embodiments, the vehicle 600 is an at least partially autonomous robot in the form of a gardening robot. In such embodiments, the vehicle 600 may use an optical sensor as a sensor 506 to determine the state of plants in the environment near the vehicle 600. The actuator 504 may be a nozzle configured to spray a chemical. Depending on the identified type of plant and / or the identified state, the actuator control command 510 may be determined to cause the actuator 504 to spray an appropriate amount of an appropriate chemical to the plant.

[0089] The vehicle 600 may be an at least partially autonomous robot in the form of a household appliance. Non-limiting examples of household appliances include a washing machine, a stove, an oven, a microwave, or a dishwasher. In such a vehicle 600, the sensor 506 may be an optical sensor configured to detect the state of an object to be processed by the household appliance. For example, in the case where the household appliance is a washing machine, the sensor 506 may detect the state of the laundry in the washing machine. The actuator control command 510 may be determined based on the detected state of the laundry.

[0090] Figure 7 A schematic diagram of a control system 502 is depicted, the control system 502 being configured to control a system 700 (e.g., a manufacturing machine), such as a punch press, a cutter, or a gun drill, of a manufacturing system 702 (e.g., a portion of a production line). The control system 502 may be configured to control an actuator 504, which is configured to control the system 700 (e.g., a manufacturing machine).

[0091] The sensor 506 of the system 700 (e.g., a manufacturing machine) may be an optical sensor configured to capture one or more attributes of the manufactured product 704. The classifier 514 may be configured to determine a state of the manufactured product 704 based on the one or more captured attributes. The actuator 504 may be configured to control the system 700 (e.g., a manufacturing machine) for subsequent manufacturing steps of the manufactured product 704 depending on the determined state of the manufactured product 704. The actuator 504 may be configured to control the function of the system 700 (e.g., a manufacturing machine) on the subsequent manufactured product 706 of the system 700 (e.g., a manufacturing machine) depending on the determined state of the manufactured product 704.

[0092] Figure 8 A schematic diagram of a control system 502 configured to control a power tool 800 , such as a drill or driver, having an at least partially autonomous mode is depicted. The control system 502 may be configured to control an actuator 504 configured to control the power tool 800 .

[0093] The sensor 506 of the power tool 800 can be an optical sensor configured to capture one or more properties of the working surface 802 and / or the fastener 804 driven into the working surface 802. The classifier 514 can be configured to determine the state of the working surface 802 and / or the fastener 804 relative to the working surface 802 based on the one or more captured properties. The state can be that the fastener 804 is flush with the working surface 802. Alternatively, the state can be the hardness of the working surface 802. The actuator 504 can be configured to control the power tool 800 so that the driving function of the power tool 800 depends on the determined state of the fastener 804 relative to the working surface 802 or one or more captured properties of the working surface 802 to adjust. For example, if the state of the fastener 804 is flush with the working surface 802, the actuator 504 can interrupt the driving function. As another non-limiting example, the actuator 504 can apply additional or less torque depending on the hardness of the working surface 802.

[0094] Fig. 9 Depicted is a schematic diagram of a control system 502 configured to control an automated personal assistant 900. The control system 502 may be configured to control an actuator 504, which is configured to control the automated personal assistant 900. The automated personal assistant 900 may be configured to control a household appliance, such as a washing machine, stove, oven, microwave, or dishwasher.

[0095] Sensor 506 may be an optical sensor and / or an audio sensor. The optical sensor may be configured to receive a video image of gesture 904 of user 902. The audio sensor may be configured to receive a voice command of user 902.

[0096] The control system 502 of the automated personal assistant 900 can be configured to determine an actuator control command 510 configured to control the system 502. The control system 502 can be configured to determine the actuator control command 510 based on the sensor signal 508 of the sensor 506. The automated personal assistant 900 is configured to transmit the sensor signal 508 to the control system 502. The classifier 514 of the control system 502 can be configured to execute a gesture recognition algorithm to identify the gesture 904 made by the user 902, determine the actuator control command 510, and transmit the actuator control command 510 to the actuator 504. The classifier 514 can be configured to retrieve information from the non-volatile storage device in response to the gesture 904 and output the retrieved information in a form suitable for receipt by the user 902.

[0097] Fig.10A schematic diagram of a control system 502 configured to control a surveillance system 1000 is depicted. The surveillance system 1000 can be configured to physically control entry through a door 1002. A sensor 506 can be configured to detect a scene relevant to a decision as to whether entry is granted. The sensor 506 can be an optical sensor configured to generate and transmit image and / or video data. The control system 502 can use such data to detect a human face.

[0098] The classifier 514 of the control system 502 of the monitoring system 1000 can be configured to interpret the image and / or video data by matching the identities of known persons stored in the non-volatile storage device 516 to determine the identity of the person. The classifier 514 can be configured to generate an actuator control command 510 in response to the interpretation of the image and / or video data. The control system 502 is configured to transmit the actuator control command 510 to the actuator 504. In this embodiment, the actuator 504 can be configured to lock or unlock the door 1002 in response to the actuator control command 510. In some embodiments, non-physical logical access control is also possible.

[0099] The monitoring system 1000 may also be a surveillance system. In such an embodiment, the sensor 506 may be an optical sensor configured to detect a scene under surveillance, and the control system 502 is configured to control the display 1004. The classifier 514 is configured to determine a classification of the scene, such as whether the scene detected by the sensor 506 is suspicious. The control system 502 is configured to transmit an actuator control command 510 to the display 1004 in response to the classification. The display 1004 may be configured to adjust the displayed content in response to the actuator control command 510. For example, the display 1004 may highlight an object that is considered suspicious by the classifier 514. Using embodiments of the disclosed system, the surveillance system can predict the appearance of an object at a specific time in the future.

[0100] Fig.11 A schematic diagram of a control system 502 is depicted, which is configured to control an imaging system 1100, such as an MRI device, an x-ray imaging device, or an ultrasound device. The sensor 506 can be, for example, an imaging sensor. The classifier 514 can be configured to determine a classification of all or part of the sensed image. The classifier 514 can be configured to determine or select an actuator control command 510 in response to the classification obtained by the trained neural network. For example, the classifier 514 can interpret an area of ​​the sensed image as a potential abnormality. In this case, the actuator control command 510 can be determined or selected to cause the display 1102 to display the imaging and highlight the potential abnormal area.

[0101] In some embodiments, a method for labeling audio data includes receiving video stream data associated with a data capture environment from at least one image capture device. The method also includes receiving audio stream data corresponding to at least a portion of the video stream data from at least one audio capture array. The method also includes labeling at least some objects of the video stream data using output from at least a first machine learning model, the first machine learning model being configured to provide output including one or more object detection predictions. The method also includes calculating at least one offset value for at least a portion of the audio stream data corresponding to at least one labeled object of the video stream data based on at least one data capture characteristic, and synchronizing at least a portion of the video stream data with a portion of the audio stream data corresponding to at least one labeled object of the video stream data using at least at least one offset value. The method also includes labeling at least a portion of the audio stream data corresponding to at least one labeled object of the video stream data using one or more tags of the labeled objects of the video stream data and at least one offset value. The method also includes generating training data using at least some of the labeled portions of the audio stream data, and training a second machine learning model using the training data.

[0102] In some embodiments, at least one audio capture array includes a plurality of audio capture devices. In some embodiments, at least one audio capture array is positioned away from at least one image capture device. In some embodiments, marking at least some objects of the video stream data using outputs from at least a first machine learning model includes marking at least some objects of the video stream data with at least an event type, an event start indicator, and an event end indicator. In some embodiments, at least one data capture characteristic includes one or more characteristics of at least one image capture device. In some embodiments, at least one data capture characteristic includes one or more characteristics of at least one audio capture array. In some embodiments, at least one data capture characteristic includes one or more characteristics corresponding to the position of at least one image capture device relative to at least one audio capture array. In some embodiments, at least one data capture characteristic includes one or more characteristics corresponding to the movement of an object in the video stream data. In some embodiments, based on at least one data capture characteristic, calculating at least one offset value of at least a portion of the audio stream data corresponding to at least one marked object of the video stream data includes using at least one probability-based function.

[0103] In some embodiments, a system for labeling audio data includes a processor and a memory. The memory includes instructions that, when executed by the processor, cause the processor to: receive video stream data associated with a data capture environment from at least one image capture device; receive audio stream data corresponding to at least a portion of the video stream data from at least one audio capture array; label at least some objects of the video stream data using output from at least a first machine learning model, the first machine learning model being configured to provide output including one or more object detection predictions; calculate at least one offset value for at least a portion of the audio stream data corresponding to at least one labeled object of the video stream data based on at least one data capture characteristic; synchronize at least a portion of the video stream data with a portion of the audio stream data corresponding to at least one labeled object of the video stream data using at least at least one offset value; label at least a portion of the audio stream data corresponding to at least one labeled object of the video stream data using one or more labels of the labeled objects of the video stream data and at least one offset value; generate training data using at least some of the labeled portions of the audio stream data; train a second machine learning model using the training data; and detect one or more sounds associated with the audio data provided as input to the second machine learning model using the second machine learning model.

[0104] In some embodiments, at least one audio capture array includes a plurality of audio capture devices. In some embodiments, at least one audio capture array is positioned away from at least one image capture device. In some embodiments, the instructions further cause the processor to use the output from at least the first machine learning model to mark at least some objects of the video stream data by marking at least some objects of the video stream data with at least an event type, an event start indicator, and an event end indicator. In some embodiments, at least one data capture characteristic includes one or more characteristics of at least one image capture device. In some embodiments, at least one data capture characteristic includes one or more characteristics of at least one audio capture array. In some embodiments, at least one data capture characteristic includes one or more characteristics corresponding to the position of at least one image capture device relative to at least one audio capture array. In some embodiments, at least one data capture characteristic includes one or more characteristics corresponding to the movement of an object in the video stream data. In some embodiments, the instructions further cause the processor to calculate at least one offset value of at least a portion of the audio stream data corresponding to at least one marked object of the video stream data based on the at least one data capture characteristic by using at least one probability-based function.

[0105] In some embodiments, an apparatus for labeling audio data includes a processor and a memory. The memory includes instructions that, when executed by the processor, cause the processor to: receive video stream data associated with a data capture environment from at least one image capture device; receive audio stream data corresponding to at least a portion of the video stream data from at least one audio capture array; label at least some objects of the video stream data with at least an event type, an event start indicator, and an event end indicator using output from at least a first machine learning model, the first machine learning model being configured to provide output including one or more object detection predictions; calculate at least one offset value for at least a portion of the audio stream data corresponding to at least one labeled object of the video stream data based on at least one data capture characteristic and using at least one probability-based function; synchronize at least a portion of the video stream data with a portion of the audio stream data corresponding to at least one labeled object of the video stream data using at least at least one offset value; label at least a portion of the audio stream data corresponding to at least one labeled object of the video stream data using one or more tags of the labeled objects of the video stream data and at least one offset value; generate training data using at least some of the labeled portions of the audio stream data; and train a second machine learning model using the training data.

[0106] In some embodiments, at least one audio capture array comprises a plurality of audio capture devices, and wherein the plurality of audio capture devices are located remotely from the at least one image capture device.

[0107] In some embodiments, a method for generating training data includes: receiving audio stream data associated with at least one audio capture array disposed in a data capture environment, receiving video stream data associated with the data capture environment simultaneously with the audio stream data, the video stream data being associated with at least one image capture array disposed in the data capture environment at a distance from the at least one audio capture array, and identifying one or more objects in the video stream data using output from at least a first machine learning model, the first machine learning model being configured to provide output including one or more object detection predictions. The method also includes, for at least one of the one or more objects identified in the video stream data, using output from a probability-based audio event detector to identify a portion of the audio stream data corresponding to the at least one object. The method also includes calculating, based on at least one data capture characteristic, at least one offset value for at least a portion of the audio stream data corresponding to at least one labeled object of the video stream data, and for the at least one object, calculating a shift value based on a cross-correlation between a portion of the video stream data corresponding to the at least one object and a portion of the audio stream data corresponding to the at least one object. The method also includes synchronizing a portion of the video stream data corresponding to the at least one object with a portion of the audio stream data corresponding to the at least one object using at least one offset value and a shift value, and marking at least a portion of the audio stream data corresponding to the at least one object using one or more tags extracted for the corresponding object of the video stream data, the at least one offset value, and the shift value. The method also includes generating training data using at least one of the marked portions of the audio stream data.

[0108] In some embodiments, at least one audio capture array includes a plurality of audio capture devices. In some embodiments, the method further includes training an audio recognition machine learning model using training data. The audio recognition machine learning model that has been trained using the training data is configured to receive an input including audio stream data and detect one or more events in the input. In some embodiments, the probability-based audio event detector includes one of a Mel-frequency cepstral coefficient feature extraction classifier and a logistic regression classifier. In some embodiments, at least one data capture characteristic includes one or more characteristics of at least one image capture device. In some embodiments, at least one data capture characteristic includes one or more characteristics of at least one audio capture array. In some embodiments, at least one data capture characteristic includes one or more characteristics corresponding to the position of at least one image capture device relative to at least one audio capture array. In some embodiments, at least one data capture characteristic includes one or more characteristics corresponding to the movement of an object in video stream data. In some embodiments, at least one data capture characteristic includes one or more probabilities of an event associated with at least one object of video stream data occurring within a period of time.

[0109] In some embodiments, a system for training an audio recognition machine learning model includes a processor and a memory. The memory includes instructions that, when executed by the processor, cause the processor to: receive audio stream data associated with at least one audio capture array disposed in a data capture environment, receive video stream data associated with the data capture environment simultaneously with the audio stream data, the video stream data being associated with at least one image capture array disposed in the data capture environment at a distance from the at least one audio capture array; use output from at least a first machine learning model to identify one or more objects in the video stream data, the first machine learning model being configured to provide output including one or more object detection predictions; for at least one of the one or more objects identified in the video stream data, use output from a probability-based audio event detector to identify a portion of the audio stream data corresponding to the at least one object; based on at least one Data capture characteristics, calculating at least one offset value for at least a portion of audio stream data corresponding to at least one marked object of video stream data; for the at least one object, calculating a shift value based on a cross-correlation between a portion of the video stream data corresponding to the at least one object and a portion of the audio stream data corresponding to the at least one object; synchronizing a portion of the video stream data corresponding to the at least one object with a portion of the audio stream data corresponding to the at least one object using at least at least one offset value and a shift value; marking at least a portion of the audio stream data corresponding to the at least one object using one or more tags extracted for the corresponding object of the video stream data, at least one offset value, and a shift value; generating training data using at least one of the marked portions of the audio stream data; and training an audio recognition machine learning model using the training data.

[0110] In some embodiments, at least one audio capture array includes multiple audio capture devices. In some embodiments, an audio recognition machine learning model that has been trained using training data is configured to receive an input including audio stream data and detect one or more events in the input. In some embodiments, a probability-based audio event detector includes one of a Mel-frequency cepstral coefficient feature extraction classifier and a logistic regression classifier. In some embodiments, at least one data capture characteristic includes one or more characteristics of at least one image capture device. In some embodiments, at least one data capture characteristic includes one or more characteristics of at least one audio capture array. In some embodiments, at least one data capture characteristic includes one or more characteristics corresponding to the position of at least one image capture device relative to at least one audio capture array. In some embodiments, at least one data capture characteristic includes one or more characteristics corresponding to the movement of an object in video stream data. In some embodiments, at least one data capture characteristic includes one or more probabilities of an event associated with at least one object of video stream data occurring within a period of time.

[0111] In some embodiments, an apparatus for training an audio recognition machine learning model includes a processor and a memory. The memory includes instructions that, when executed by the processor, cause the processor to: receive audio stream data associated with at least one audio capture device disposed in a data capture environment; receive video stream data associated with the data capture environment simultaneously with the audio stream data, the video stream data being associated with at least one image capture device disposed in the data capture environment at a distance from the at least one audio capture device; use output from at least a first machine learning model to identify one or more objects in the video stream data, the first machine learning model being configured to provide output including one or more object detection predictions; for at least one of the one or more objects identified in the video stream data, use output from a probability-based audio event detector to identify a portion of the audio stream data corresponding to the at least one object; and calculate, based on at least one data capture characteristic, an audio stream corresponding to at least one labeled object in the video stream data. at least one offset value for at least a portion of streaming data; for the at least one object, calculating a shift value based on a cross-correlation between a portion of video stream data corresponding to the at least one object and a portion of audio stream data corresponding to the at least one object; synchronizing the portion of video stream data corresponding to the at least one object with a portion of audio stream data corresponding to the at least one object using at least at least one offset value and a shift value; marking at least a portion of the audio stream data corresponding to the at least one object using one or more tags extracted for the corresponding object of the video stream data, at least one offset value, and a shift value; generating training data using at least one of the marked portions of the audio stream data; and training an audio recognition machine learning model using the training data, the audio recognition machine learning model that has been trained using the training data being configured to detect one or more events in the audio stream data provided as input to the audio recognition machine learning model.

[0112] In some embodiments, the at least one data capture characteristic includes one or more probabilities that an event associated with at least one object of the video stream data occurs within a time period.

[0113] The process, method or algorithm disclosed herein can be delivered to / implemented by a processing device, a controller or a computer, and the processing device, the controller or the computer can include any existing programmable electronic control unit or a dedicated electronic control unit. Similarly, the process, method or algorithm can be stored in various forms as data and instructions that can be executed by a controller or a computer, including but not limited to information permanently stored on a non-writable storage medium such as a ROM device and information that can be variably stored on a writable storage medium such as a floppy disk, a tape, a CD, a RAM device, and other magnetic and optical media. The process, method or algorithm can also be implemented in a software executable object. Alternatively, a suitable hardware component (such as an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a state machine, a controller) or other hardware components or devices, or a combination of hardware, software and firmware components, can be used to embody the process, method or algorithm in whole or in part.

[0114] Although exemplary embodiments are described above, it is not intended that these embodiments describe all possible forms covered by the claims. The words used in the specification are descriptive rather than restrictive words, and it should be understood that various changes can be made without departing from the spirit and scope of the present disclosure. As previously described, the features of the various embodiments can be combined to form other embodiments of the present invention that may not be explicitly described or illustrated. Although various embodiments may have been described as providing advantages or preferred over other embodiments or prior art implementations in terms of one or more desired characteristics, it is recognized by those of ordinary skill in the art that one or more features or characteristics can be compromised to achieve the desired overall system properties, depending on the specific application and implementation. These properties may include, but are not limited to, cost, strength, durability, life cycle cost, marketability, appearance, packaging, size, applicability, weight, manufacturability, ease of assembly, etc. As such, to the extent that any embodiment is described as less desirable than other embodiments or prior art implementations in terms of one or more characteristics, these embodiments are not outside the scope of the present disclosure and may be desirable for a particular application.

Claims

1. A method for generating training data, the method comprising: receiving audio stream data associated with at least one audio capture array disposed in a data capture environment; receiving video stream data associated with the data capture environment simultaneously with the audio stream data, the video stream data being associated with at least one image capture array disposed in the data capture environment at a distance from at least one audio capture array; identifying one or more objects in the video stream data using output from at least a first machine learning model, the first machine learning model configured to provide output including one or more object detection predictions; for at least one of the one or more objects identified in the video stream data, identifying a portion of the audio stream data corresponding to the at least one object using an output from a probability-based audio event detector; calculating, based on at least one data capture characteristic, at least one offset value for at least a portion of the audio stream data corresponding to at least one marked object of the video stream data; For the at least one object, calculating a shift value based on a cross-correlation between a portion of the video stream data corresponding to the at least one object and a portion of the audio stream data corresponding to the at least one object; synchronizing a portion of the video stream data corresponding to the at least one object with a portion of the audio stream data corresponding to the at least one object using at least the at least one offset value and the shift value; marking at least a portion of the audio stream data corresponding to the at least one object using the one or more tags extracted for the corresponding object of the video stream data, the at least one offset value, and the shift value; as well as Training data is generated using at least one of the marked portions of the audio stream data. 2 . The method of claim 1 , wherein the at least one audio capture array comprises a plurality of audio capture devices.

3. The method of claim 1 , further comprising training an audio recognition machine learning model using the training data, wherein the audio recognition machine learning model that has been trained using the training data is configured to: receiving input comprising audio streaming data; and Detect one or more events in the input. 4 . The method of claim 1 , wherein the probability-based audio event detector comprises one of a Mel-frequency cepstral coefficient feature extraction classifier and a logistic regression classifier.

5. The method of claim 1, wherein the at least one data capture characteristic comprises one or more characteristics of the at least one image capture device.

6. The method of claim 1, wherein the at least one data capture characteristic comprises one or more characteristics of the at least one audio capture array.

7. The method of claim 1, wherein the at least one data capture characteristic comprises one or more characteristics corresponding to a position of the at least one image capture device relative to the at least one audio capture array.

8. The method of claim 1, wherein the at least one data capture characteristic comprises one or more characteristics corresponding to movement of objects in the video stream data.

9. The method of claim 1, wherein the at least one data capture characteristic comprises one or more probabilities of an event associated with at least one object of the video stream data occurring within a time period.

10. A system for training an audio recognition machine learning model, the system comprising: Processor; and A memory comprising instructions that, when executed by a processor, cause the processor to: receiving audio stream data associated with at least one audio capture array disposed in a data capture environment; receiving video stream data associated with the data capture environment simultaneously with the audio stream data, the video stream data being associated with at least one image capture array disposed in the data capture environment at a distance from at least one audio capture array; identifying one or more objects in the video stream data using output from at least a first machine learning model, the first machine learning model configured to provide output including one or more object detection predictions; for at least one of the one or more objects identified in the video stream data, identifying a portion of the audio stream data corresponding to the at least one object using an output from a probability-based audio event detector; calculating, based on at least one data capture characteristic, at least one offset value for at least a portion of the audio stream data corresponding to at least one marked object of the video stream data; For the at least one object, calculating a shift value based on a cross-correlation between a portion of the video stream data corresponding to the at least one object and a portion of the audio stream data corresponding to the at least one object; synchronizing a portion of the video stream data corresponding to the at least one object with a portion of the audio stream data corresponding to the at least one object using at least one offset value and a shift value; marking at least a portion of the audio stream data corresponding to the at least one object using the one or more tags, at least one offset value, and a shift value extracted for a corresponding object of the video stream data; generating training data using at least one of the marked portions of the audio stream data; and Use the training data to train the audio recognition machine learning model.

11. The system of claim 10, wherein the at least one audio capture array comprises a plurality of audio capture devices.

12. The system of claim 10, wherein the audio recognition machine learning model that has been trained using the training data is configured to: receiving input comprising audio streaming data; and Detect one or more events in the input.

13. The system of claim 10, wherein the probability-based audio event detector comprises one of a Mel-frequency cepstral coefficient feature extraction classifier and a logistic regression classifier.

14. The system of claim 10, wherein the at least one data capture characteristic comprises one or more characteristics of the at least one image capture device.

15. The system of claim 10, wherein the at least one data capture characteristic comprises one or more characteristics of the at least one audio capture array.

16. The system of claim 10, wherein the at least one data capture characteristic comprises one or more characteristics corresponding to a position of the at least one image capture device relative to the at least one audio capture array.

17. The system of claim 10, wherein the at least one data capture characteristic includes one or more characteristics corresponding to movement of objects in the video stream data.

18. The system of claim 10, wherein the at least one data capture characteristic comprises one or more probabilities of an event associated with at least one object of the video stream data occurring within a time period.

19. An apparatus for training an audio recognition machine learning model, the apparatus comprising: Processor; and A memory comprising instructions that, when executed by a processor, cause the processor to: receiving audio stream data associated with at least one audio capture device disposed in a data capture environment; receiving video stream data associated with the data capture environment simultaneously with the audio stream data, the video stream data being associated with at least one image capture device disposed in the data capture environment at a distance from at least one audio capture device; identifying one or more objects in the video stream data using output from at least a first machine learning model, the first machine learning model configured to provide output including one or more object detection predictions; for at least one of the one or more objects identified in the video stream data, identifying a portion of the audio stream data corresponding to the at least one object using an output from a probability-based audio event detector; calculating, based on at least one data capture characteristic, at least one offset value for at least a portion of the audio stream data corresponding to at least one marked object of the video stream data; For the at least one object, calculating a shift value based on a cross-correlation between a portion of the video stream data corresponding to the at least one object and a portion of the audio stream data corresponding to the at least one object; synchronizing a portion of the video stream data corresponding to the at least one object with a portion of the audio stream data corresponding to the at least one object using at least one offset value and a shift value; marking at least a portion of the audio stream data corresponding to the at least one object using the one or more tags, at least one offset value, and a shift value extracted for a corresponding object of the video stream data; generating training data using at least one of the marked portions of the audio stream data; and The audio recognition machine learning model is trained using the training data, and the audio recognition machine learning model that has been trained using the training data is configured to detect one or more events in the audio stream data provided as input to the audio recognition machine learning model.

20. The apparatus of claim 19, wherein the at least one data capture characteristic comprises one or more probabilities of an event associated with at least one object of the video stream data occurring within a time period.

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