Abstract: Audio processing systems and methods comprise an audio sensor array configured to receive a multichannel audio input and generate a corresponding multichannel audio signal and a target activity detector configured to identify audio target sources in the multichannel audio signal. The target activity detector includes a VAD, an instantaneous locations component configured to detect a location of a plurality of audio sources, a dominant locations component configured to selectively buffer a subset of the plurality of audio sources comprising dominant audio sources, a source tracker configured to track locations of the dominant audio sources over time, and a dominance selection component configured to select the dominant target sources for further audio processing. The instantaneous location component computes a discrete spatial map comprising the location of the plurality of audio sources, and the dominant location component selects N of the dominant sources from the discrete spatial map for source tracking.

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 audio processing device or method includes an audio transducer operable to receive audio input and generate an audio signal based on the audio input. The audio processing device or method also includes an audio signal processor operable to extract local features from the audio signal, such as Power-Normalized Coefficients (PNCC) of the audio signal. The audio signal processor also is operable to extract global features from the audio signal, such as chroma features and harmonicity features. A neural network is provided to determine a probability that a target audio is present in the audio signal based on the local and global features. In particular, the neural network is trained to output a value indicating whether the target audio is present and locally dominant in the audio signal.

Abstract: Systems and methods for processing an audio signal include an audio input operable to receive an input signal comprising a time-domain, single-channel audio signal, a subband analysis block operable to transform the input signal to a frequency domain input signal comprising a plurality of k-spaced under-sampled subband signals, a reverberation reduction block operable to reduce reverberation effect, including late reverberation, in the plurality of k-spaced under-sampled subband signals, a noise reduction block operable to reduce background noise from the plurality of k-spaced under-sampled subband signals, and a subband synthesis block operable to transform the subband signals to the time-domain, thereby producing an enhanced output signal.

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 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: 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: 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: Audio signal processing for adaptive de-reverberation uses a least mean squares (LMS) filter that has improved convergence over conventional LMS filters, making embodiments practical for reducing the effects of reverberation for use in many portable and embedded devices, such as smartphones, tablets, laptops, and hearing aids, for applications such as speech recognition and audio communication in general. The LMS filter employs a frequency-dependent adaptive step size to speed up the convergence of the predictive filter process, requiring fewer computational steps compared to a conventional LMS filter applied to the same inputs. The improved convergence is achieved at low memory consumption cost. Controlling the updates of the prediction filter in a high non-stationary condition of the acoustic channel improves the performance under such conditions. The techniques are suitable for single or multiple channels and are applicable to microphone array processing.

Abstract: Various implementations disclosed herein include an expert-assisted phoneme recognition neural network system configured to recognize phonemes within continuous large vocabulary speech sequences without using language specific models (“left-context”), look-ahead (“right-context”) information, or multi-pass sequence processing, and while operating within the resource constraints of low-power and real-time devices. To these ends, in various implementations, an expert-assisted phoneme recognition neural network system as described herein utilizes a-priori phonetic knowledge. Phonetics is concerned with the configuration of the human vocal tract while speaking and acoustic consequences on vocalizations. While similar sounding phonemes are difficult to detect and are frequently misidentified by previously known neural networks, phonetic knowledge gives insight into what aspects of sound acoustics contain the strongest contrast between similar sounding phonemes.

Abstract: Various implementations disclosed herein include an expert-assisted phoneme recognition neural network system configured to recognize phonemes within continuous large vocabulary speech sequences without using language specific models (“left-context”), look-ahead (“right-context”) information, or multi-pass sequence processing, and while operating within the resource constraints of low-power and real-time devices. To these ends, in various implementations, an expert-assisted phoneme recognition neural network system as described herein utilizes a-priori phonetic knowledge. Phonetics is concerned with the configuration of the human vocal tract while speaking and acoustic consequences on vocalizations. While similar sounding phonemes are difficult to detect and are frequently misidentified by previously known neural networks, phonetic knowledge gives insight into what aspects of sound acoustics contain the strongest contrast between similar sounding phonemes.

Abstract: Various implementations disclosed herein include an expert-assisted phoneme recognition neural network system configured to recognize phonemes within continuous large vocabulary speech sequences without using language specific models (“left-context”), look-ahead (“right-context”) information, or multi-pass sequence processing, and while operating within the resource constraints of low-power and real-time devices. To these ends, in various implementations, an expert-assisted phoneme recognition neural network system as described herein utilizes a-priori phonetic knowledge. Phonetics is concerned with the configuration of the human vocal tract while speaking and acoustic consequences on vocalizations. While similar sounding phonemes are difficult to detect and are frequently misidentified by previously known neural networks, phonetic knowledge gives insight into what aspects of sound acoustics contain the strongest contrast between similar sounding phonemes.

Abstract: Various implementations disclosed herein include an expert-assisted phoneme recognition neural network system configured to recognize phonemes within continuous large vocabulary speech sequences without using language specific models (“left-context”), look-ahead (“right-context”) information, or multi-pass sequence processing, and while operating within the resource constraints of low-power and real-time devices. To these ends, in various implementations, an expert-assisted phoneme recognition neural network system as described herein utilizes a-priori phonetic knowledge. Phonetics is concerned with the configuration of the human vocal tract while speaking and acoustic consequences on vocalizations. While similar sounding phonemes are difficult to detect and are frequently misidentified by previously known neural networks, phonetic knowledge gives insight into what aspects of sound acoustics contain the strongest contrast between similar sounding phonemes.