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.

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: 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. A mixture with reduced reverberation of the target direct-path signal is obtained by removing the result of applying the filter to the first estimate of the target direct-path signal from the received mixture. A second DNN produces a second estimate of the target direct-path signal from the mixture with reduced reverberation.

Abstract: Embodiments of the present disclosure disclose a system and method for extraction of a target sound signal. The system collects collect a mixture of sound signals. The system selects a query identifying the target sound signal to be extracted from the mixture of sound signals, the query comprising one or more identifiers. Each identifier is present in a predetermined set of one or more identifiers and defines at least one of mutually inclusive and mutually exclusive characteristics of the mixture of sound signals. The system determined one or more logical operators connecting the extracted one or more identifiers. The system transforms the one or more identifiers and the extracted logical operators into a digital representation. The system executes a neural network trained to extract the target sound signal by mixing the digital representation with intermediate outputs of intermediate layers of the neural network.

Abstract: A system and method for low-latency audio signal enhancement is provided. An input mixture of audio signals is partitioned into a sequence of overlapping frames by using a first sliding window method. The first sliding window method comprises a first window function having a first width associated with a window of the corresponding frame and a shift length associated with shifting of the window of the first sliding window method. Each frame is then processed using a first DNN, a frequency domain causal linear filter and a second DNN, to generate final enhanced overlapping frames for each of the processed frames. The final enhanced overlapping frames are then combined using a second sliding window method associated with a second window function having a second width less than the first width and the same shift length as the first sliding window method.

Abstract: An audio processing system is provided. The audio processing system comprises an input interface configured to accept an audio signal. Further, the audio processing system comprises a memory configured to store a neural network trained to determine different types of attributes of multiple concurrent audio events of different origins, wherein the types of attributes include time-dependent and time-agnostic attributes of speech and non-speech audio events. Further, the audio processing system comprises a processor configured to process the audio signal with the neural network to produce metadata of the audio signal, the metadata including one or multiple attributes of one or multiple audio events in the audio signal.

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: A controller and a method for optimizing a controlled operation of a system performing a task is provided. The method for optimizing the controlled operation of the system comprises accessing a probabilistic distribution of a performance function trained to provide a relationship between different combinations of control parameters for controlling the system and their corresponding costs of operation, selecting a combination of control parameters from the different combinations of control parameters, such that the selected combination of control parameters is having the largest likelihood of being optimal at the probabilistic distribution of the performance function. The method further comprises controlling the system using the selected combination of the control parameters and modifying the probabilistic distribution of the performance function conditioned on the selected combination of the control parameters and the corresponding cost of operation.

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 for controlling an operation of a machine including a plurality of actuators assisting one or multiple tools to perform one or multiple tasks, in response to receiving an acoustic mixture of signals generated by the tool performing a task and by the plurality of actuators actuating the tool, submit the acoustic mixture of signals into a neural network trained to separate from the acoustic mixture a signal generated by the tool performing the task from signals generated by the actuators actuating the tool to extract the signal generated by the tool performing the task from the acoustic mixture of signals, analyze the extracted signal to produce a state of performance of the task, and execute a control action selected according to the state of performance of the task.

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. A mixture with reduced reverberation of the target direct-path signal is obtained by removing the result of applying the filter to the first estimate of the target direct-path signal from the received mixture. A second DNN produces a second estimate of the target direct-path signal from the mixture with reduced reverberation.