Abstract: Systems and methods for an audio signal processing system for transforming an input audio signal. A processor implements steps of a module by inputting an input audio signal into a spectrogram estimator to extract an audio feature sequence, and process the audio feature sequence to output a set of estimated spectrograms. Processing the set of estimated spectrograms and the audio feature sequence using a spectrogram refinement module, to output a set of refined spectrograms. Wherein the processing of the spectrogram refinement module is based on an iterative reconstruction algorithm. Processing the set of refined spectrograms for the one or more target audio signals using a signal refinement module, to obtain the target audio signal estimates. An output interface to output the optimized target audio signal estimates. Wherein the module is optimized by minimizing an error using an optimizer stored in the memory.

Abstract: Systems and methods for a speech recognition system for recognizing speech including overlapping speech by multiple speakers. The system including a hardware processor. A computer storage memory to store data along with having computer-executable instructions stored thereon that, when executed by the processor is to implement a stored speech recognition network. An input interface to receive an acoustic signal, the received acoustic signal including a mixture of speech signals by multiple speakers, wherein the multiple speakers include target speakers. An encoder network and a decoder network of the stored speech recognition network are trained to transform the received acoustic signal into a text for each target speaker. Such that the encoder network outputs a set of recognition encodings, and the decoder network uses the set of recognition encodings to output the text for each target speaker. An output interface to transmit the text for each target speaker.

Abstract: A method for training a multi-language speech recognition network includes providing utterance datasets corresponding to predetermined languages, inserting language identification (ID) labels into the utterance datasets, wherein each of the utterance datasets is labelled by each of the language ID labels, concatenating the labeled utterance datasets, generating initial network parameters from the utterance datasets, selecting the initial network parameters according to a predetermined sequence, and training, iteratively, an end-to-end network with a series of the selected initial network parameters and the concatenated labeled utterance datasets until a training result reaches a threshold.

Abstract: A method for performing multi-label classification includes extracting a feature vector from an input vector including input data by a feature extractor, determining, by a label predictor, a relevant vector including relevant labels having relevant scores based on the feature vector, updating a binary masking vector by masking pre-selected labels having been selected in previous label selections, applying the updated binary masking vector to the relevant vector such that the relevant label vector is updated to exclude the pre-selected labels from the relevant labels, and selecting a relevant label from the updated relevant label vector based on the relevant scores of the updated relevant label vector.

Abstract: A method transforms a noisy audio signal to an enhanced audio signal, by first acquiring the noisy audio signal from an environment. The noisy audio signal is processed by an enhancement network having network parameters to jointly produce a magnitude mask and a phase estimate. Then, the magnitude mask and the phase estimate are used to obtain the enhanced audio signal.

Abstract: A method distinguishes components of a signal by processing the signal to estimate a set of analysis features, wherein each analysis feature defines an element of the signal and has feature values that represent parts of the signal, processing the signal to estimate input features of the signal, and processing the input features using a deep neural network to assign an associative descriptor to each element of the signal, wherein a degree of similarity between the associative descriptors of different elements is related to a degree to which the parts of the signal represented by the elements belong to a single component of the signal. The similarities between associative descriptors are processed to estimate correspondences between the elements of the signal and the components in the signal. Then, the signal is processed using the correspondences to distinguish component parts of the signal.

Abstract: A method estimates source signals from a mixture of source signals by first training an analysis model and a reconstruction model using training data. The analysis model is applied to the mixture of source signals to obtain an analysis representation of the mixture of source signals, and the reconstruction model is applied to the analysis representation to obtain an estimate of the source signals, wherein the analysis model utilizes an analysis linear basis representation, and the reconstruction model utilizes a reconstruction linear basis representation.

Abstract: In an acoustic apparatus, an acoustic transducer is arranged in a substrate. Multiple acoustic pathways in the substrate have predetermined lengths, wherein a proximal end of each pathway forms an opening in a front surface of the substrate, and a distal end terminates at the acoustic transducer. The predetermined lengths of the acoustic pathways are designed to form an acoustic spatial filter that selectively passes acoustic signals from or to different locations. The transducer can convert electric energy to acoustic energy when the apparatus operates as a speaker, or the the transducer can convert acoustic energy to electric energy and operate as a microphone.

Abstract: A method processes an acoustic signal that is a mixture of a target signal and interfering signals by first enhancing the acoustic signal by a set of enhancement procedures to produce a set of initial enhanced signals. Then, an ensemble learning procedure is applied to the acoustic signal and the set of initial enhancement signals to produce features of the acoustic signal.

Abstract: A method for transforms input signals, by first defining a model for transforming the input signals, wherein the model is specified by constraints and a set of model parameters. An iterative inference procedure is derived from the model and the set of model parameters and unfolded into a set of layers, wherein there is one layer for each iteration of the procedure, and wherein a same set of network parameters is used by all layers. A neural network is formed by untying the set of network parameters such that there is one set of network parameters for each layer and each set of network parameters is separately maintainable and separately applicable to the corresponding layer. The neural network is trined to obtain a trained neural network, and then input signals are transformed using the trained neural network to obtain output signals.

Abstract: A method for transforms input signals, by first defining a model for transforming the input signals, wherein the model is specified by constraints and a set of model parameters. An iterative inference procedure is derived from the model and the set of model parameters and unfolded into a set of layers, wherein there is one layer for each iteration of the procedure, and wherein a same set of network parameters is used by all layers. A neural network is formed by untying the set of network parameters such that there is one set of network parameters for each layer and each set of network parameters is separately maintainable and separately applicable to the corresponding layer. The neural network is trained to obtain a trained neural network, and then input signals are transformed using the trained neural network to obtain output signals.

Abstract: A method distinguishes components of a signal by processing the signal to estimate a set of analysis features, wherein each analysis feature defines an element of the signal and has feature values that represent parts of the signal, processing the signal to estimate input features of the signal, and processing the input features using a deep neural network to assign an associative descriptor to each element of the signal, wherein a degree of similarity between the associative descriptors of different elements is related to a degree to which the parts of the signal represented by the elements belong to a single component of the signal. The similarities between associative descriptors are processed to estimate correspondences between the elements of the signal and the components in the signal. Then, the signal is processed using the correspondences to distinguish component parts of the signal.

Abstract: In an acoustic apparatus, an acoustic transducer is arranged in a substrate. Multiple acoustic pathways in the substrate have predetermined lengths, wherein a proximal end of each pathway forms an opening in a front surface of the substrate, and a distal end terminates at the acoustic transducer. The predetermined lengths of the acoustic pathways are designed to form an acoustic spatial filter that selectively passes acoustic signals from or to different locations. The transducer can convert electric energy to acoustic energy when the apparatus operates as a speaker, or the the transducer can convert acoustic energy to electric energy and operate as a microphone.

Abstract: A method detects events in an accoustic signal subject to cyclostationary background noise by first segmenting the signal into cycles. Features with a fixed dimension are derived from the cycles, such that the timing of the features is relative to a cycle time. The features are normalized using an estimate of the cyclostationary background noise. Then, after the normalizing, a classifier is applied to the features to detect the events.

Abstract: An information system includes a prediction engine for predicting an action based on a set of driving state parameters, and a driving history, and a simulation engine for generating a hypothetical scenario by simulating one or a combination of at least one driving state parameter and at least part of the driving history, such that the prediction engine predicts the action for the hypothetical scenario.

Abstract: A method distinguishes components of an acoustic signal by processing the signal to estimate a set of analysis features, wherein each analysis feature defines an element of the signal and has feature values that represent parts of the signal, processing the signal to estimate input features of the signal, and processing the input features using a deep neural network to assign an associative descriptor to each element of the signal, wherein a degree of similarity between the associative descriptors of different elements is related to a degree to which the parts of the signal represented by the elements belong to a single component of the signal. The the similarities between associative descriptors are processed to estimate correspondences between the elements of the signal and the components in the signal. Then, the signal is processed using the correspondences to distinguish component parts of the signal.

Abstract: A method determines from an input noisy signal sequences of hidden variables including at least one sequence of hidden variables representing an excitation component of the clean speech signal, at least one sequence of hidden variables representing a filter component of the clean speech signal, and at least one sequence of hidden variables representing the noise signal. The sequences of hidden variables include hidden variables determined as a non-negative linear combination of non-negative basis functions. The determination uses the model of the clean speech signal that includes a non-negative source-filter dynamical system (NSFDS) constraining the hidden variables representing the excitation and the filter components to be statistically dependent over time. The method generates an output signal using a product of corresponding hidden variables representing the excitation and the filter components.

Abstract: A method transforms a noisy speech signal to an enhanced speech signal, by first acquiring the noisy speech signal from an environment. The noisy speech signal is processed by an automatic speech recognition system (ASR) to produce ASR features. The the ASR features and noisy speech spectral features are processed using an enhancement network having network parameters to produce a mask. Then, the mask is applied to the noisy speech signal to obtain the enhanced speech signal.

Abstract: A method transforms a noisy audio signal to an enhanced audio signal, by first acquiring the noisy audio signal from an environment. The noisy audio signal is processed by an enhancement network having network parameters to jointly produce a magnitude mask and a phase estimate. Then, the magnitude mask and the phase estimate are used to obtain the enhanced audio signal.

Abstract: A method for transforms input signals, by first defining a model for transforming the input signals, wherein the model is specified by constraints and a set of model parameters. An iterative inference procedure is derived from the model and the set of model parameters and unfolded into a set of layers, wherein there is one layer for each iteration of the procedure, and wherein a same set of network parameters is used by all layers. A neural network is formed by untying the set of network parameters such that there is one set of network parameters for each layer and each set of network parameters is separately maintainable and separately applicable to the corresponding layer. The neural network is trined to obtain a trained neural network, and then input signals are transformed using the trained neural network to obtain output signals.