Northwestern Polytechnical University

Wevoice Inc.

Jacob Benesty

Jingdong Chen

Gongping Huang

Jilu Jin

Yiteng Huang

Chao Pan

Xudong Zhao

Yuzhu Wang

Xueqin Luo

A concentric circular microphone array (CCMA) may include a number of omnidirectional microphones and an equal number of directional microphones, wherein the omnidirectional microphones and the directional microphones form a plurality of concentric rings on a substantially planar platform. Each of the plurality of concentric rings includes a subset of the omnidirectional microphones and a subset of the directional microphones (e.g., arranged in mixed pairs of microphones). Responsive to a sound source, the omnidirectional microphones and the directional microphones may respectively generate first and second electronic signals. A target beampattern of Norder may be specified for the CCMA. An Norder beamformer for the CCMA, that is steerable in a three-dimensional space including the sound source, may be determined based on the specified target beampattern. The beamformer may be executed to calculate an estimate of the sound source based on the first electronic signals and the second electronic signals.

Concentric circular microphone arrays with 3D steerable beamformers

A binaural beamformer comprising two beamforming filters may be communicatively coupled to a microphone array to generates two beamforming outputs, one for the left ear and the other for the right ear. The beamforming filters may be configured in such a way that they are orthogonal to each other to make white noise components in the binaural outputs substantially uncorrelated and desired signal components in the binaural outputs highly correlated. As a result, the human auditory system may better separate the desired signal from white noise and intelligibility of the desired signal may be improved.

Binaural beamforming microphone array

A differential microphone array includes a plurality of microphones situated on a substantially planar platform and a processing device, communicatively coupled to the plurality of microphones, to receive a plurality of electronic signals generated by the plurality of microphones responsive to a sound source and execute a minimum-norm beamformer to calculate an estimate of the sound source based on the plurality of electronic signals, wherein the minimum-norm beamformer is determined subject to a constraint that an approximation of a beampattern associated with the differential microphone array substantially matches a target beampattern.

Flexible geographically-distributed differential microphone array and associated beamformer

A differential microphone array includes a plurality of microphones situated on a substantially planar platform, the plurality of microphones including a total number (M) of microphones and at least two subsets of the plurality of microphones situated along at least two substantially concentric ellipses with respect to a center, and a processing device, communicatively coupled to the plurality of microphones, to receive a plurality of electronic signals generated by the plurality of microphones responsive to a sound source and execute a minimum-norm beamformer to calculate an estimate of the sound source based on the plurality of electronic signals, in which the minimum-norm beamformer has a differential order (N), and wherein M&#x3e;N+1.

Concentric circular differential microphone arrays and associated beamforming

A differential microphone array includes a number (M) of microphone sensors for converting sound to a number of electrical signals, and a processor, operably coupled to the microphone sensors, to specify a target differential order (N) for the differential microphone array, and wherein M&#x3e;N+1, specify a steering matrix D comprising N+1 steering vectors, calculate a respective one of a plurality of linearly specify a steering matrix D comprising N+1 steering vectors-constrained minimum variance filters based on the steering matrix, apply the respective one of the plurality of linearly-constrained minimum variance filters to a respective one of the electrical signals to calculate a respective frequency response of the electrical signals, wherein the respective frequency response comprises a plurality of components associated with a plurality of subbands, and sum the frequency responses of the electrical signals with respect to each subband to calculate an estimated frequency spectrum of the sound.

Low noise differential microphone arrays

A system and method relate to receiving, by a processing device, a plurality of sound signals captured at a plurality of microphone sensors, wherein the plurality of sound signals are from a sound source, and wherein a number (M) of the plurality of microphone sensors is greater than three, determining a number (K) of layers for a multistage minimum variance distortionless response (MVDR) beamformer based on the number (M) of the plurality of microphone sensors, wherein the number (K) of layers is greater than one, and wherein each layer of the multistage MVDR beamformer comprises one or more mini-length MVDR beamformers, and executing the multistage MVDR beamformer to the plurality of sound signals to calculate an estimate of the sound source.

Multistage minimum variance distortionless response beamformer

A differential microphone array (DMA) is provided that includes a number (M) of microphone sensors for converting a sound to a number of electrical signals and a processor that is configured to apply linearly-constrained minimum variance filters on the electrical signals over a time window to calculate frequency responses of the electrical signals over a plurality of subbands and sum the frequency responses of the electrical signals for each subband to calculate an estimated frequency spectrum of the sound.

A system and method may receive a single-channel speech input captured via a microphone. For each current frame of speech input, the system and method may (a) perform a time-frequency transformation on the input signal over L (L&#x3e;1) frames including the current frame to obtain an extended observation vector of the current frame, data elements in the extended observation vector representing the coefficients of the time-frequency transformation of the L frames of the speech input, (b) compute second-order statistics of the extended observation vector and of noise, and (c) construct a noise reduction filter for the current frame of the speech input based on the second-order statistics of the extended observation vector and the second-order statistics of noise.

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Montreal, Canada

Jacob Benesty