Abstract: A system and method of generating binaural signals includes receiving, by a processing device, a sound signal including speech and noise components, and transforming, by the processing device using a deep neural network (DNN), the sound signal into a first signal and a second signal. The transforming further includes encoding, by an encoding layer of the DNN, the sound signal into a sound signal representation in a latent space, rendering, by a rendering layer of the DNN, the sound signal representation into a first signal representation and a second signal representation in the latent space, and decoding, by a decoding layer of the DNN, the first signal representation into the first signal and the second signal representation into the second signal.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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>N+1.

Abstract: 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>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.

Abstract: 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.

Abstract: 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.

Abstract: 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>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.

Abstract: 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.

Abstract: 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.

Abstract: 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>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.

Abstract: 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>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.

Abstract: The invention is a method and apparatus for frequency-domain adaptive filtering that has broad applications such as to equalizers, but is particularly suitable for use in acoustic echo cancellation circuits for stereophonic and other multiple channel teleconferencing systems. The method and apparatus utilizes a frequency-domain recursive least squares criterion that minimizes the error signal in the frequency-domain. In order to reduce the complexity of the algorithm, a constraint is removed resulting in an unconstrained frequency-domain recursive least mean squares method and apparatus. A method and apparatus for selecting an optimal adaptation step for the UFLMS is disclosed. The method and apparatus is generalized to the multiple channel case and exploits the cross-power spectra among all of the channels.

Abstract: The invention is a method and apparatus for frequency-domain adaptive filtering that has broad applications such as to equalizers, but is particularly suitable for use in acoustic echo cancellation circuits for stereophonic and other multiple channel teleconferencing systems. The method and apparatus utilizes a frequency-domain recursive least squares criterion that minimizes the error signal in the frequency-domain. In order to reduce the complexity of the algorithm, a constraint is removed resulting in an unconstrained frequency-domain recursive least mean squares method and apparatus. A method and apparatus for selecting an optimal adaptation step for the UFLMS is disclosed. The method and apparatus is generalized to the multiple channel case and exploits the cross-power spectra among all of the channels.

Abstract: The invention is a method and apparatus for frequency-domain adaptive filtering that has broad applications such as to equalizers, but is particularly suitable for use in acoustic echo cancellation circuits for stereophonic and other multiple channel teleconferencing systems. The method and apparatus utilizes a frequency-domain recursive least squares criterion that minimizes the error signal in the frequency-domain. In order to reduce the complexity of the algorithm, a constraint is removed resulting in an unconstrained frequency-domain recursive least mean squares method and apparatus. A method and apparatus for selecting an optimal adaptation step for the UFLMS is disclosed. The method and apparatus is generalized to the multiple channel case and exploits the cross-power spectra among all of the channels.

Abstract: A signal detection technique for multiple-input multiple-output (MIMO) communications systems embodied in a method and apparatus for detecting a plurality of transmitted signals with use of a plurality of receiving antennas. An iterative procedure decodes one of a plurality of transmitted signals at each iteration using an intermediate matrix at each iteration to determine the transmitted signal to be decoded. The intermediate matrix for each successive iteration is advantageously computed in a recursive manner with use of a Schur complement operation performed based on the inverse of a modified version of the intermediate matrix used in the previous iteration.

Abstract: A robust adaptive filter for use in a network echo canceller or other digital signal processing application utilizes a coefficient vector update device that, through the application of fast converging algorithms to a fast impulse response filter yields fast convergence of the adaptive filter's characteristics with the avoidance of divergence due to the onset of double talk. Robustness is also provided, via an adaptive scale non-linearity device which applies an adaptive scale non-linearity to the filter algorithms fed to the fast impulse response filter by the coefficient vector update device, so that the samples of an echo signal to be cancelled which are taken during the onset of double talk can be handled in such a manner that after the double talk detector causes adaptation to cease, the initial, potentially disturbing samples do not cause significant divergence in the filter system.

Abstract: A method for calculating an estimate of the clock skew between a sender's clock and a receiver's clock in a packet-based communications network. An adaptive algorithm is employed in which a recursive least squares approach is used to calculate an estimate of the clock skew based on the transmission of a given (i e., the “current”) packet, which estimate is further based on a previous estimate thereof (“a first approximation” thereof). This illustrative process then iterates with each new packet, producing increasingly accurate estimates of the clock skew.