Synaptics Corporation

Alireza Masnadi-Shirazi

Francesco Nesta

Saeed Mosayyebpour Kaskari

This disclosure provides methods, devices, and systems for beamforming. The present implementations more specifically relate to semi-adaptive beamforming techniques. In some aspects, a semi-adaptive beamformer may determine an RTF vector based on an audio signal received via a microphone array (also referred to as an 'instantaneous' RTF vector) and may further determine an MVDR beamforming filter for the microphone array based on a combination of the instantaneous RTF vector and a “fixed” RTF vector. The fixed RTF vector may include a set of RTFs that are known to produce a relatively accurate MVDR beamforming filter for any users of the microphone array. In some implementations, the semi-adaptive beamformer may determine the MVDR beamforming filter based on a weighted average of the instantaneous RTF vector and the fixed RTF vector, where the weighting can be dynamically adjusted based on the quality of the received audio signal or various other conditions.

Semi-adaptive beamformer

This disclosure provides methods, devices, and systems for signal processing. The present implementations relate more specifically to a spatio-temporal beamformer. In some aspects, a beamforming system may receive an audio signal via a plurality of microphones, the audio signal including a number (B) of frames for each of the plurality of microphones, each of the B frames for each of the plurality of microphones including a number (N) of time-domain samples. For a first microphone, the beamforming system may transform the B*N time-domain samples into B*N/2 first frequency-domain samples; transform the B*N/2 first frequency-domain samples into B*N/2 second frequency-domain samples; and determine a probability of speech associated with the B*N/2 second frequency-domain samples based on a neural network model. The beamformer system may determine a minimum variance distortionless response (MVDR) beamforming filter based at least in part on the probability of speech for the first microphone.

Spatio-temporal beamformer

Embodiments described herein provide a combined multi-source time difference of arrival (TDOA) tracking and voice activity detection (VAD) mechanism that is applicable for generic array geometries, e.g., a microphone array that lies on a plane. The combined multi-source TDOA tracking and VAD mechanism scans the azimuth and elevation angles of the microphone array in microphone pairs, based on which a planar locus of physically admissible TDOAs can be formed in the multi-dimensional TDOA space of multiple microphone pairs. In this way, the multi-dimensional TDOA tracking reduces the number of calculations that was usually involved in traditional TDOA by performing the TDOA search for each dimension separately.

Multiple-source tracking and voice activity detections for planar microphone arrays

Systems and methods include a plurality of audio input components configured to generate a plurality of audio input signals, and a logic device configured to receive the plurality of audio input signals, determine whether the plurality of audio signals comprise target audio associated with an audio source, estimate a relative location of the audio source with respect to the plurality of audio input components based on the plurality of audio signals and a determination of whether the plurality of audio signals comprise the target audio, and process the plurality of audio signals to generate an audio output signal by enhancing the target audio based on the estimated relative location. The logic device is further configured to use relative transfer-based covariance to construct directional covariance matrix aligned across frequency bands and find a direction that minimizes beam power subject to distortionless criteria.

Robust speaker localization in presence of strong noise interference systems and methods

Systems and methods include a first voice activity detector operable to detect speech in a frame of a multichannel audio input signal and output a speech determination, a constrained minimum variance adaptive filter operable to receive the multichannel audio input signal and the speech determination and minimize a signal variance at the output of the filter, thereby producing an equalized target speech signal, a mask estimator operable to receive the equalized target speech signal and the speech determination and generate a spectral-temporal mask to discriminate a target speech from noise and interference speech, and a second activity voice detector operable to detect voice in a frame of the speech discriminated signal. An audio input sensor array including a plurality of microphones, each microphone generating a channel of the multichannel audio input signal. A sub-band analysis module operable to decompose each of the channels into a plurality of frequency sub-bands.

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Irvine, CA, United States of America

Alireza Masnadi-Shirazi