Cirrus Logic Inc.

Acoustic Technologies, Inc.

Acoustic Technology, Inc.

Cirrus Logic International Semiconductor Limited

Samuel P Ebenezer

Seth Suppappola

Rachid Kerkoud

Ghassan Maalouli

William E Sherwood

Wilbur Lawrence

Clifton Cordes

John L Melanson

Franklyn H Story

Justin Lamar Allen

John Bryan-Merrett

Mert Salahi

A method for intelligently generating a stimulus for use in characterization of parameters of a model of a battery may include dynamically analyzing a current drawn from the battery by a load, based on analysis of the current, determining an augmented current for augmenting the current drawn by the load, and generating the augmented current based on a determined need to update the parameters.

Online characterization of battery model parameters with augmented dynamic stimulus

An adaptive noise-canceling system generates an anti-noise signal with a filter that has a response controlled by a set of coefficients selected from a collection of coefficient sets. The adaptive noise-canceling system includes an acoustic output transducer for reproducing a signal containing the anti-noise signal, a first microphone for measuring ambient noise at a first location to produce a first noise measurement signal, a second microphone for measuring the ambient noise at a second location to generate a second noise measurement signal, and an analysis subsystem for analyzing the first noise measurement signal and the second noise measurement signal. The adaptive noise-canceling system also includes a controller that selects the set of coefficients from the collection of coefficient sets according to a phase difference between the first noise measurement signal and the second noise measurement signal as determined by the analysis subsystem.

Adaptive noise-canceling with dynamic filter selection based on multiple noise sensor signal phase differences

An adaptive noise-canceling system generates an anti-noise signal from a noise reference signal with a feed-forward filter that filters the noise reference signal to produce the anti-noise signal. The feed-forward filter has a first response controlled by a set of first coefficients. The adaptive noise-canceling system includes a measurement subsystem for measuring a characteristic of an acoustic environment of the adaptive noise-canceling system, a classifier for classifying the characteristic of the acoustic environment by analyzing an output of the measurement subsystem, and a controller that provides the set of first coefficients to the feed-forward filter in conformity with an output of the classifier. The controller may include a look-up table for providing sets of values of the first coefficients to the feed-forward filter in conformity with an indication provided from the classifier and corresponding to a classification of the characteristic of the acoustic environment of the adaptive noise-canceling system.

Feed-forward adaptive noise-canceling with dynamic filter selection based on classifying acoustic environment

A method of identifying a system, the method comprising: obtaining an indication of background noise present at the system; generating a probe signal based on the indication; applying the probe signal to the system; estimating a response of the system to the probe signal; and identifying the system based on the measured response and the probe signal, wherein the probe signal comprises a whitening component configured to whiten noise in the estimated response due to the background noise present at the system.

Methods and apparatus for system identification

A method for generating an acoustic stimulus for use in an ear biometric process on a user, the method comprising: receiving an indication of stimulation frequencies for use in the ear biometric process; grouping the stimulation frequencies into bands of a psychoacoustic scale; generating the acoustic stimulus, the acoustic stimulus comprising a masked bandpass component within each band of the psychoacoustic scale that comprises one or more of the stimulation frequencies.

Methods and apparatus for biometric processes

An integrated circuit for implementing at least a portion of a personal audio device may include an output for providing a signal to a transducer including both a source audio signal for playback to a listener and an anti-noise signal for countering the effects of ambient audio sounds in an acoustic output of the transducer, a reference microphone input for receiving a reference microphone signal indicative of the ambient audio sounds, an error microphone input for receiving an error microphone signal indicative of the output of the transducer and the ambient audio sounds at the transducer, and a processing circuit configured to implement an adaptive infinite impulse response filter having a response that generates the anti-noise signal to reduce the presence of the ambient audio sounds at the error microphone and implement a coefficient control block that shapes the response of the adaptive infinite impulse response filter in conformity with the error microphone signal by generating coefficients that determine the response of the adaptive infinite impulse response filter in order to minimize the ambient audio sounds at the error microphone, wherein the coefficient control block selects the coefficients from a library of filter entries, each filter entry of the library of filter entries defining a respective response for the adaptive infinite impulse response filter.

Active noise cancellation system using infinite impulse response filtering

Voice biometrics scoring is performed on received asynchronous audio outputs from microphones distributed at ad hoc locations to generate confidence scores that indicate a likelihood of an enrolled user speech utterance in the output, a subset of the outputs is selected based on the confidence scores, and the subset is spatially processed to provide audio output for voice application use. Alternatively, asynchronous spatially processed audio outputs and corresponding biometric identifiers are received from corresponding devices distributed at ad hoc locations, audio frames of the outputs are synchronized using the biometric identifiers, and the synchronized frames are coherently combined. Alternatively, uttered speech associated with respective ad hoc distributed devices is received and non-coherently combined to generate a final output of uttered speech. The uttered speech is recognized from respective spatially processed outputs generated by the respective devices using biometrics of talkers enrolled by the devices.

Asynchronous ad-hoc distributed microphone array processing in smart home applications using voice biometrics

Methods and apparatus for digital signal processing of signals received from sensors are provided. A first input signal and a second input signal are received. A noise correlation statistic between the first input signal and the second input signal is estimated. An inter sensor signal model representative of a relationship between desired signal components present in the first input signal and the second input signal is estimated. Responsive to the noise correlation statistic meeting a predefined condition, estimating the inter sensor signal model is based on the noise correlation statistic. Responsive to the noise correlation statistic not meeting the predefined condition, estimating the inter sensor signal model is based on a constrained noise correlation statistic derived from the noise correlation statistic.

Methods and apparatus for an adaptive blocking matrix

A method includes extracting, from multiple microphone input, a hyperset of features of acoustic sources, using the extracted features to identify separable clusters associated with acoustic scenarios, and classifying subsequent input as one of the acoustic scenarios using the hyperset of features. The acoustic scenarios include a desired spatially moving/non-moving talker, and an undesired spatially moving/non-moving acoustic source. The hyperset of features includes both spatial and voice biometric features. The classified acoustic scenario may be used in a robotics application or voice assistant device desired speech enhancement or interference signal cancellation. Specifically, the classification of the acoustic scenarios can be used to adapt a beamformer, e.g., step size adjustment. The hyperset of features may also include visual biometric features extracted from one or more cameras viewing the acoustic sources. The spatial and biometric features may be separately extracted, clustered, classified and their separate classifications fused, e.g., using frame synchronization.

Acoustic source classification using hyperset of fused voice biometric and spatial features

An adaptive beam-forming array uses multiple sensors and noise reference subtraction to reduce noise at an output of the adaptive beam-forming array. A direction of arrival of energy from a desired source is determined and an inter-sensor noise correlation between one or more pairs of sensors is determined. An Adaptive Blocking Matrix (ABM) generates a noise reference from an inter-sensor model representing a relationship between desired signal components received from the desired source and that are present in signals from one or more pairs of sensors. The noise reference is generated with an adaptive filter that filters a first signal from a first sensor in the pairs of sensors and is combined with the second signal from a second sensor in the pairs of sensors to produce the noise reference. The adaptive filter is initialized with an initialization response computed from the direction of arrival and the inter-sensor noise correlation.

Initialization of adaptive blocking matrix filters in a beamforming array using a priori information

An audio device has an array of microphones and a voice processing system that obtains a multi-dimensional spatial feature vector comprising at least a correlation of the microphones and a calculation of at least one ratio of energies of the microphones, uses the multi-dimensional feature vector to estimate an energy of near-field speech and background noise, uses a ratio of the near-field speech energy and background noise estimates to estimate a probability of a presence of the near-field speech, adaptively combines signals from the microphones based on the estimated near-field speech presence probability to provide a combined output signal comprising a near-field speech signal and a residual background noise signal, estimates a power spectral density of the residual background noise signal present at the combined output signal using the estimated near-field speech presence probability, and reduces the background noise by using the estimated power spectral density.

Noise reduction system and method for audio device with multiple microphones

In accordance with embodiments of the present disclosure, a method for detecting near-field sources in an audio device may include computing a normalized cross correlation function between a first microphone signal and a second microphone signal, computing normalized auto correlation functions of each of the first microphone signal and the second microphone signal, partitioning the normalized cross correlation function and the normalized auto correlation functions into a plurality of time lag regions, computing for each respective time lag region of the plurality of the time lag regions a respective maximum deviation between the normalized cross correlation function and a normalized auto correlation function within the respective time lag region, combining the respective maximum deviations from the plurality of time lag regions to derive multiple detection statistics, and comparing each detection statistic of the multiple detection statistics to a respective threshold to detect a near-field signal.

Correlation-based near-field detector

In accordance with embodiments of the present disclosure, a method for voice processing in an audio device having an array of a plurality of microphones wherein the array is capable of having a plurality of positional orientations relative to a user of the array, is provided. The method may include periodically computing a plurality of normalized cross-correlation functions, each cross-correlation function corresponding to a possible orientation of the array with respect to a desired source of speech, determining an orientation of the array relative to the desired source based on the plurality of normalized cross-correlation functions, detecting changes in the orientation based on the plurality of normalized cross-correlation functions, and responsive to a change in the orientation, dynamically modifying voice processing parameters of the audio device such that speech from the desired source is preserved while reducing interfering sounds.

Dual microphone voice processing for headsets with variable microphone array orientation

A method may include determining a desired speech estimate originating from a speech acceptance direction range while reducing a level of interfering noise, determining an interfering noise estimate originating from a noise rejection direction range while reducing a level of desired speech, calculating a ratio of the desired speech estimate to the interfering noise estimate, dynamically computing a set of thresholds based on the speech acceptance direction range, noise rejection direction range, a background noise level, and a noise type, estimating a power spectral density of background noise arriving from the noise rejection direction range, calculating a frequency-dependent gain function based on the power spectral density of background noise and thresholds, and applying the frequency-dependent gain function to at least one microphone signal generated by the plurality of microphones to reduce noise arriving from the noise rejection direction while preserving desired speech arriving from the speech acceptance direction.

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Gilbert, AZ, United States of America

Samuel P Ebenezer