Abstract: Systems, methods, software, and devices are disclosed herein that transform spatial input into modal output comprising learned modal components of an impulse response. A neural network interpolates the modal components of the impulse response based on a desired sound source direction represented in the spatial input. The learned modal components are then used to determine coefficients for an infinite impulse response filter that transforms anechoic audio into spatialized audio. The spatialized audio provides a directional effect to a listener as having arrived from the desired sound source direction.

Abstract: An audio processing system is disclosed for comparing a query audio sample with a database of multiple reference audio samples using an external normalization. The system includes at least one processor and memory storing instructions that, when executed by the processor, cause the system to determine a bias term of the external normalization based on a spectro-temporal pattern of the query audio sample. The system further compares the query audio sample with each of the reference audio samples to generate a similarity score for each comparison. The system combines the bias term with each of the similarity scores to produce normalized similarity scores. The normalized similarity scores are then compared with a threshold to generate a result of comparison, which is subsequently outputted.

Abstract: A system for performing end-to-end automatic speech recognition (ASR). The system configured to collect a sequence of acoustic frames associated with a mixture of speeches performed by multiple speakers. Each frame from the sequence of acoustic frames is encoded using a multi-head encoder which encodes each frame into a likelihood of a transcription output and a likelihood of an identity of a speaker. The multi-head encoder thus produces a sequence of likelihoods of transcription outputs and a sequence of likelihoods of identities of the speakers corresponding to the sequence of acoustic frames that are decoded using a decoder performing an alignment operation for producing a sequence of transcription outputs annotated with identities of the speakers, for performing speaker separation.

Abstract: The present disclosure provides an audio system, a method and a system for facilitating operation of a machine. The machine includes actuators assisting tools to perform tasks. In an example, the audio system is configured to receive an audio mixture of signals generated by audio sources including at least one of the tools performing the tasks, or the actuators. The audio sources forming the audio mixture are identified by a location relative to a location of each microphone of a microphone array measuring the audio mixture. The audio system is configured to extract an audio signal from the audio mixture generated by an identified audio source, based on a correlation of spectral features in a multi-channel spectrogram of the audio mixture with directional information indicative of the relative location of the identified audio source. The audio system outputs the extracted audio signal to facilitate the operation of the machine.

Abstract: A computer-implemented method for detecting anomaly of an operation of a machine based on a signal indicative of the operation of the machine performing a task, comprises collecting hyperbolic embeddings of the signal indicative of the operation of the machine. The hyperbolic embeddings lie in a hyperbolic space. The method further comprises performing the detection of the anomaly of the operation of the machine based on the hyperbolic embeddings to determine an anomaly score and rendering the anomaly score. The machine operation is controlled based on the rendered anomaly score.

Abstract: A system and method for automatically generating and rendering a report data structure is provided. The report data structure is formed in a platform independent manner that includes all data for transactions used in the report. The system analyzes the transactions to be included in the report and selects the type of display component based on a ranking score to best highlight the data contained therein.

Abstract: A system and method for sound processing for performing multi-talker conversation analysis is provided. The sound processing system includes a deep neural network trained for processing audio segments of an audio mixture of the multi-talker conversation. The deep neural network includes a speaker-independent layer that produces a speaker-independent output, and a speaker-biased layer applied once independently to each of the audio segments for each multiple speakers of the audio mixture. The deep neural network also processes a time-invariant embedding by individually assigning each application of the speaker-biased layer to a corresponding speaker by inputting the corresponding time-invariant speaker embedding. The deep neural network thus produces data indicative of time-frequency activity regions of each speaker of the multiple speakers in the audio mixture from a combination of speaker-biased outputs.

Abstract: A method, a system and a computer program product are provided for applying a neural network including an action sequence decoder for generating an action sequence for a robot to perform a task. The neural network is applied to generate the action sequence based on recordings demonstrating humans performing tasks. In an example, the method comprises collecting a recording and a sequence of captions describing scenes in the recording; extracting feature data from the recording; encoding the extracted feature data to produce a sequence of encoded features; and applying the action sequence decoder to produce a sequence of actions for the robot based on the sequence of encoded features having a semantic meaning corresponding to a semantic meaning of the sequence of captions. The feature data includes features of a video signal, an audio signal, and/or text transcription capturing a performance of the task.

Abstract: Embodiments disclose a method and system for a scene-aware audio-video representation of a scene. The scene-aware audio video representation corresponds to a graph of nodes connected by edges. A node in the graph is indicative of the video features of an object in the scene. An edge in the graph connecting two nodes indicates an interaction of the corresponding two objects in the scene. In the graph, at least one or more edges are associated with audio features of a sound generated by the interaction of the corresponding two objects. The graph of the audio-video representation of the scene may be used to perform a variety of different tasks. Examples of the tasks include one or a combination of an action recognition, an anomaly detection, a sound localization and enhancement, a noisy-background sound removal, and a system control.

Abstract: There is provided an audio processing system and method comprising an input interface that receives an input audio mixture and transforms it into a time-frequency representation defined by values of time-frequency bins, a processor that maps the values of time-frequency bins into a hyperbolic space by executing an embedding neural network trained to associate each time-frequency bin to a high-dimensional embedding and projecting each high-dimensional embedding into the hyperbolic space, and an output interface that accepts a selection of at least a portion of the hyperbolic space and renders selected hyperbolic embeddings falling within the selected portion of the hyperbolic space.

Abstract: An audio processing system and method for processing audio is disclosed. The audio processing system collects an input audio signal indicative of degraded measurements of a target audio waveform. The input audio signal is restored with recursive restoration that recursively restores the input audio signal until a termination condition is met. A current iteration of the recursive restoration applies a restoration operator configured to restore a degraded audio signal conditioned on a current level of severity of degradation and degrades the degraded audio signal deterministically with a level of severity less than the current level of severity. A target signal estimate indicative of enhanced measurements of the audio waveform is generated as output.

Abstract: A system for performing end-to-end automatic speech recognition (ASR). The system configured to collect a sequence of acoustic frames associated with a mixture of speeches performed by multiple speakers. Each frame from the sequence of acoustic frames is encoded using a multi-head encoder which encodes each frame into a likelihood of a transcription output and a likelihood of an identity of a speaker. The multi-head encoder thus produces a sequence of likelihoods of transcription outputs and a sequence of likelihoods of identities of the speakers corresponding to the sequence of acoustic frames that are decoded using a decoder performing an alignment operation for producing a sequence of transcription outputs annotated with identities of the speakers, for performing speaker separation.

Abstract: This invention relates generally to speech processing and more particularly to end-to-end automatic speech recognition (ASR) that utilizes long contextual information. Some embodiments of the invention provide a system and a method for end-to-end ASR suitable for recognizing long audio recordings such as lecture and conversational speeches. This disclosure includes a Transformer-based ASR system that utilizes contextual information, wherein the Transformer accepts multiple utterances at the same time and predicts transcript for the last utterance. This is repeated in a sliding-window fashion with one-utterance shifts to recognize the entire recording. In addition, some embodiments of the present invention may use acoustic and/or text features obtained from only the previous utterances spoken by the same speaker as the last utterance when the long audio recording includes multiple speakers.

Abstract: A system and method for detecting anomalous sound are disclosed. The method includes receiving a spectrogram of an audio signal with elements defined by values in a time-frequency domain of the spectrogram. Each of the values corresponds to an element of the spectrogram that is identified by a coordinate in the time-frequency domain. The time-frequency domain of the spectrogram is partitioned into a context region and a target region. The context region and the target region are processed by a neural network using an attentive neural process to recover values of the spectrogram for elements with coordinates in the target region. The recovered values of the elements of the target region are compared with values of elements of the partitioned target region. An anomaly score is determined based on the comparison. The anomaly score is used for performing a control action.

Abstract: A system and method for reverberation reduction is disclosed. A first Deep Neural Network (DNN) produces a first estimate of a target direct-path signal from a mixture of acoustic signals that include the target direct-path signal and a reverberation of the target direct-path signal. A filter modeling a room impulse response (RIR) for the first estimate is estimated. The filter when applied to the first estimate of the target direct-path signal generates a result closest to a residual between the mixture of the acoustic signals and the first estimate of the target direct-path signal according to a distance function. The estimated filter is used for modeling the RIR.

Abstract: An artificial intelligence (AI) low-latency processing system is provided. The low-latency processing system includes a processor; and a memory having instructions stored thereon. The low-latency processing system is configured to collect a sequence of frames jointly including information dispersed among at least some frames in the sequence of frames, execute a timing neural network trained to identify an early subsequence of frames in the sequence of frames including at least a portion of the information indicative of the information, and execute a decoding neural network trained to decode the information from the portion of the information in the subsequence of frames, wherein the timing neural network is jointly trained with the decoding neural network to iteratively identify the smallest number of subframes from the beginning of a training sequence of frames containing a portion of training information sufficient to decode the training information.