Abstract: Embodiments disclosing an audio processing system for isolating and extracting a varying number of sound sources from an audio mixture are provided. The audio processing system includes a prompt input interface configured to produce a set of input digital encodings representing input sound prompts of at least some of the sound sources forming the audio mixture in a space of the features of the audio mixture. The set of input digital encodings includes a set of target digital encodings representing target sound prompts for extracting target sound sources from the audio mixture. An information exchanger neural network is trained to modify each of the set of target digital encodings and the features of the audio mixture. An extraction neural network is trained to extract a varying number of the target sound sources by processing the modified target digital encodings and the modified features of the audio mixture.

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: Systems, methods, software, and devices are disclosed herein that transform anechoic audio signals into spatialized audio signals. An audio processing method includes identifying a target sound source direction and a reference head related transfer function (HRTF) associated with a target subject and obtaining one or more retrieved HRTFs from an HRTF dataset based at least on the reference HRTF and the target sound source direction. The method continues with executing a neural field model to produce an output based on an input. Example input includes the one or more retrieved HRTFs and the target sound source direction, and example output includes a predicted HRTF. The anechoic audio signal may then be processed based at least on the predicted HRTF to produce a spatialized audio signal.

Abstract: A method and system for supervised training of a causal neural network for a streaming audio processing application is provided. The method comprises acquiring an input mixture signal corresponding to two or more speakers. Further, the method comprises training the causal neural network to transform the input mixture signal into an output signal matching a ground truth signal. To that end, the training comprises processing the input mixture signal conditioned on a causal input including a delayed version of the input mixture signal transformed by the causal neural network without the causal input.

Abstract: Systems, methods, software, and devices are disclosed herein process context data to encode one or more semantic elements of a desired audio composition in a semantic token sequence, process the semantic token sequence to encode one or more structural elements of the desired audio composition in a structural token sequence disentangled from the semantic token sequence, and process the structural token sequence to encode one or more audio signal elements of the desired audio composition in an audio signal token sequence disentangled from the structural token sequence. The semantic token sequence, the structural token sequence, and the audio signal token sequence may then be processed to generate at least a portion of the desired audio composition.

Abstract: A system for event detection in time-series data comprises a memory configured to store computer-executable instructions and one or more processors configured to execute the instructions to process the time-series data to make a hard decision on a time span of an event indicative of continuous activity of the event within the time-series data and make a soft decision on a presence of the event for the entire time span. The one or more processors are further configured to apply an event-level threshold to the soft decision on the presence of the event for the entire time span to produce a result of the event detection and output the result of the event detection.

Abstract: A system and a method for detecting anomalous sound are disclosed. The method includes receiving an audio signal from a sound source in a recording environment. The sound source and the recording environment are characterized by a set of attributes including a first attribute pertaining to a first attribute type and a second attribute pertaining to a second attribute type. A multi-head neural network is trained to extract from the received audio signal a first embedding vector indicative of the first attribute type and a second embedding vector indicative of the second attribute type. The first embedding vector is compared with a first set of embedding vectors to classify attributes of the first attribute type and the second embedding vector is compared with a second set of embedding vectors to classify attributes of the second attribute type, to determine a result of anomaly detection.

Abstract: Embodiments of the present disclosure disclose a system and method for localization of a target sound event. The system collects a first digital representation of an acoustic mixture of sounds of a plurality of sound events, by using an acoustic sensor. The system receives a second digital representation of a sound corresponding to the target sound event. Further, the first digital representation and the second digital representation are processed by a neural network to produce a localization information indicative of a location of an origin of the target sound event with respect to a location of the acoustic sensor.

Abstract: An audio system for synthesizing audio sounds having a desired audio trait executes an autoregressive generative audio transformer trained for generating the audio by processing inputs with multiple layers employing multi-head attention, and uses directional inference-time intervention (ITI) to push at least some outputs of at least some heads of the multi-head attention into a direction predetermined for the desired audio trait.

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: 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: The predictive controller determines, using the deep generative decoder model, a conditional probabilistic distribution of the latent representations of the disturbance conditioned on the partial observations of the disturbance, and samples the conditional probabilistic distribution of the latent representations to produce a latent sample of the time-series values of the disturbance affecting the mechanical system over the time horizon. The predictive controller decodes the latent sample with the deep generative decoder model to produce predicted values of the disturbance acting on the system within the time horizon with a probability of the latent sample on the conditional probabilistic distribution of the latent representations and controls the mechanical system using a predictive controller that determines control commands changing a state of the operation of the mechanical system using the probability of at least some of the predicted values of the disturbance.

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: 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 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: 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 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.