Abstract: Systems and methods provide input and output mode control for audio processing on a user device. Audio processing may be configured by monitoring audio activity on a device having at least one microphone and a digital audio processing unit, collecting information from the monitoring of the activity, including an identification of at least one application utilizing audio processing, and determining a context for the audio processing, the context including at least one of a hardware, software, audio signal and/or environmental context. An audio signal processing configuration is determined based on the application and determined context, an associated audio signal processing mode is selected, and an optimized audio signal generated.

Abstract: Systems and methods for enhancing a headset user's own voice include an outside microphone, an inside microphone, audio input components operable to receive a plurality of time-domain microphone signals, including an outside microphone signal from the outside microphone and an inside microphone signal from the inside microphone, a subband decomposition module operable to transform the time-domain microphone signals to frequency domain subband signals, a voice activity detector operable to detect speech presence and absence in the subband signals, a speech extraction module operable to predict a clean speech signal in each of the inside microphone signal and the outside microphone signal, and cancel audio sources other than a headset user's own voice by combining the predicted clean speech signal from the inside microphone signal and the predicted clean speech signal from the outside microphone signal, and a postfiltering module operable to reduce residual noise.

Abstract: Classification training systems and methods include a neural network for classification of input data, a training dataset providing segmented labeled training data, and a classification training module operable to train the neural network using the training data. A forward pass processing module is operable to generate neural network outputs for the training data using weights and bias for the neural network, and a backward pass processing module is operable to update the weights and biases in a backward pass, including obtaining Region of Target (ROT) information from the training data, generate a forward-backward masking based on the ROT information, the forward-backward masking placing at least one restriction on a neural network output path, compute modified forward and backward variables based on the neural network outputs and the forward-backward masking, and update the weights and biases.

Abstract: A classification system and method for training a neural network includes receiving a stream of segmented, labeled training data having a sequence of frames, computing a stream of input features data for the sequence of frames, and generating neural network outputs for the sequence of frames in a forward pass through the training data and in accordance weights and biases. The weights and biases are updated in a backward pass through the training data, including determining Region of Target (ROT) information from the segmented, labeled training data, computing modified forward and backward variables based on the neural network outputs and the ROT information, deriving a signal error for each frame within the sequence of frames based on the modified forward and backward variables, and updating the weights and biases based on the derived signal error. An adaptive learning module is provided to improve a convergence rate of the neural network.

Abstract: A classification training system for binary and multi-class classification comprises a neural network operable to perform classification of input data, a training dataset including pre-segmented, labeled training samples, and a classification training module operable to train the neural network using the training dataset. The classification training module includes a forward pass processing module, and a backward pass processing module. The backward pass processing module is operable to determine whether a current frame is in a region of target (ROT), determine ROT information such as beginning and length of the ROT and update weights and biases using a cross-entropy cost function and connectionist temporal classification cost function. The backward pass module further computes a soft target value using ROT information and computes a signal output error using the soft target value and network output value.

Abstract: Methods for processing a multichannel audio signal that includes transient noise signals are provided. The method includes buffering the multichannel audio signal in a subband domain, and estimating the subband frames for transient noise likelihood. A probability of transient noise for the buffered subband frames is determined and a multichannel spatial filter is applied to decompose the subband frames to transient attenuated target source and noise estimation cancelled of the target source signal. A spectral filter is applied to the target source frame to enhance the target source frame and the subband frames that are determined to have a probability of the transient noise greater than a first threshold and a probability of target source less than a second threshold are muted.

Abstract: A method for echo cancellation in multichannel audio signals includes receiving a plurality of time-domain signals, including multichannel audio signals and at least one reference signal, transforming the time-domain signals to K under-sampled complex-valued subband signals using an analysis filter bank. A probability of acoustic echo dominance is produced using a single-double talk estimator, and a multichannel source separation is performed based on the probability to decompose the audio signals into a near-end source signal and a residual echoes using source separation. The residual echo components are removed from the near-end source signal using a spectral filter bank, and the subband audio signals are reconstructed to a multichannel time-domain audio signal using a subband synthesis filter.

Abstract: Systems and methods for processing multichannel audio signals include receiving a multichannel time-domain audio input, transforming the input signal to plurality of multi-channel frequency domain, k-spaced under-sampled subband signals, buffering and delaying each channel, saving a subset of spectral frames for prediction filter estimation at each of the spectral frames, estimating a variance of the frequency domain signal at each of the spectral frames, adaptively estimating the prediction filter in an online manner using a recursive least squares (RLS) algorithm, linearly filtering each channel using the estimated prediction filter, nonlinearly filtering the linearly filtered output signal to reduce residual reverberation and the estimated variances, producing a nonlinearly filtered output signal, and synthesizing the nonlinearly filtered output signal to reconstruct a dereverberated time-domain multi-channel audio signal.

Abstract: An active noise cancellation system includes a sensor operable to sense environmental noise and generate a corresponding reference signal, a fixed noise cancellation filter including a predetermined model of the active noise cancellation system operable to generate an anti-noise signal, and a tunable noise cancellation filter operable to modify the anti-noise signal in accordance with stored coefficients, wherein the tunable noise cancellation filter is further operable to modify the stored coefficients in real-time based on user feedback and generate a tuned anti-noise signal that models tunable deviations from the predetermined noise model. A graphical user interface is operable to receive user adjustments of tunable parameters in real-time, the tunable parameters corresponding to at least one of the stored coefficients.

Abstract: A system for processing audio data comprising an amplifier configured to receive an audio signal and to perform nonlinear processing on the audio signal. An encoder coupled to the amplifier, the encoder configured to receive the nonlinearly processed audio signal and to encode the nonlinearly processed audio signal into a data transmission format. A transmitter configured to receive and transmit the encoded nonlinearly processed audio signal. A receiver configured to receive the transmitted encoded nonlinearly processed audio signal and to decode the encoded nonlinearly processed audio signal. A digital voice processor configured to receive the nonlinearly processed audio signal and to use the nonlinearly processed audio signal for echo estimation and to subsequently subtract the estimated echo signal from a microphone signal.

Abstract: A system for audio processing comprising an initial background statistical model system configured to generate an initial background statistical model using a predetermined sample size of audio data. A parameter computation system configured to generate parametric data for the audio data including cepstral and energy parameters. A background statistics computation system configured to generate preliminary background statistics for determining whether speech has been detected. A first speech detection system configured to determine whether speech was present in the initial sample of audio data. An adaptive background statistical model system configured to provide an adaptive background statistical model for use in continuous processing of audio data for speech detection. A parameter computation system configured to calculate cepstral parameters, energy parameters and other suitable parameters for speech detection.

Abstract: A method for echo cancellation in multichannel audio signals includes receiving a plurality of time-domain signals, including multichannel audio signals and at least one reference signal, transforming the time-domain signals to K under-sampled complex-valued subband signals using an analysis filter bank. A probability of acoustic echo dominance is produced using a single-double talk estimator, and a multichannel source separation is performed based on the probability to decompose the audio signals into a near-end source signal and a residual echoes using source separation. The residual echo components are removed from the near-end source signal using a spectral filter bank, and the subband audio signals are reconstructed to a multichannel time-domain audio signal using a subband synthesis filter.

Abstract: A method for echo cancellation in multichannel audio signals includes receiving a plurality of time-domain signals, including multichannel audio signals and at least one reference signal, transforming the time-domain signals to K under-sampled complex-valued subband signals using an analysis filter bank, and performing, for each of the K under-sampled complex-value subband signals, linear echo cancellation of the reference signal from each channel using an acoustic echo canceller. A probability of acoustic echo dominance is produced using a single-double talk estimator, and a semi-blind multichannel source separation is performed based on the probability and independent component analysis (“ICA”) to decompose the audio signals into a near-end source signal and a residual echoes using subband semi-blind source separation.

Abstract: A selective audio source enhancement system includes a processor and a memory, and a pre-processing unit configured to receive audio data including a target audio signal, and to perform sub-band domain decomposition of the audio data to generate buffered outputs. In addition, the system includes a target source detection unit configured to receive the buffered outputs, and to generate a target presence probability corresponding to the target audio signal, as well as a spatial filter estimation unit configured to receive the target presence probability, and to transform frames buffered in each sub-band into a higher resolution frequency-domain. The system also includes a spectral filtering unit configured to retrieve a multichannel image of the target audio signal and noise signals associated with the target audio signal, and an audio synthesis unit configured to extract an enhanced mono signal corresponding to the target audio signal from the multichannel image.

Abstract: Presented is a method and associated system for suppression of linear and nonlinear echo. The method includes dividing an input signal into several frequency bands in each of a several of time frames. The input signal may include an echo signal. The method further includes multiplying the input signal in each of the several frequency bands by a corresponding echo suppression signal. Calculating the corresponding echo suppression signal may include estimating a power of the echo signal in a particular frequency band as a sum of several component echo powers, each of the several component echo powers due to an excitation from a far-end signal in a corresponding one of the several frequency bands. Calculating the corresponding echo suppression signal may further include subtracting the power of the echo signal in the particular frequency band from a power of the input signal in the particular frequency band.

Abstract: Methods for processing a multichannel audio signal that includes transient noise signals are provided. The method includes buffering the multichannel audio signal in a subband domain, and estimating the subband frames for transient noise likelihood. A probability of transient noise for the buffered subband frames is determined and a multichannel spatial filter is applied to decompose the subband frames to transient attenuated target source and noise estimation cancelled of the target source signal. A spectral filter is applied to the target source frame to enhance the target source frame and the subband frames that are determined to have a probability of the transient noise greater than a first threshold and a probability of target source less than a second threshold are muted.

Abstract: Various techniques are provided to perform enhanced automatic speech recognition. For example, a subband analysis may be performed that transforms time-domain signals of multiple audio channels in subband signals. An adaptive configurable transformation may also be performed to produce single or multichannel-based features whose values are correlated to an Ideal Binary Mask (IBM). An unsupervised Gaussian Mixture Model (GMM) model fitting the distribution of the features and producing posterior probabilities may also be performed, and the posteriors may be combined to produce deep neural network (DNN) feature vectors. A DNN may be provided that predicts oracle spectral gains from the input feature vectors. Spectral processing may be performed to produce an estimate of the target source time-frequency magnitudes from the mixtures and the output of the DNN. Subband synthesis may be performed to transform signals back to time-domain.

Abstract: Systems and methods provide input and output mode control for audio processing on a user device. Audio processing may be configured by monitoring audio activity on a device having at least one microphone and a digital audio processing unit, collecting information from the monitoring of the activity, including an identification of at least one application utilizing audio processing, and determining a context for the audio processing, the context including at least one context resource having associated metadata. An audio configuration is determined based on the application and determined context, and an action is performed to control the audio processing mode. User controls providing addition mode control may be displayed automatically based on a current application and determined context.

Abstract: A selective audio source enhancement system includes a processor and a memory, and a pre-processing unit configured to receive audio data including a target audio signal, and to perform sub-band domain decomposition of the audio data to generate buffered outputs. In addition, the system includes a target source detection unit configured to receive the buffered outputs, and to generate a target presence probability corresponding to the target audio signal, as well as a spatial filter estimation unit configured to receive the target presence probability, and to transform frames buffered in each sub-band into a higher resolution frequency-domain. The system also includes a spectral filtering unit configured to retrieve a multichannel image of the target audio signal and noise signals associated with the target audio signal, and an audio synthesis unit configured to extract an enhanced mono signal corresponding to the target audio signal from the multichannel image.

Abstract: A system for controlling distortion comprising a total harmonic distortion (THD) modeling system configured to apply a chirp signal to a system and to identify one or more frequency bands at which distortion is present, and to apply a ramping signal to identify for each of the one or more frequency bands an input signal level at which distortion is initiated. A signal processing system configured to receive an input signal, to determine whether frequency components are present in the input signal that are associated with the one or more frequency bands at which distortion is present, and to limit the amplitude of the input signal at the one or more frequency bands, such as by applying dynamic range compensation.