Cirrus Logic Inc.

Acoustic Technologies, Inc.

General Dynamics C4 Systems, Inc.

Cirrus Logic International Semiconductor Limited

Justin Lamar Allen

Khosrow Lashkari

Franklyn H Story

Seth Suppappola

Steven M Domer

Samuel L Thomasson

Samuel P Ebenezer

Ghassan Maalouli

Narayan Kovvali

Keith Charles Palermo

Nima Yousefian Jazi

Kellie Michele Vanda

A wind noise suppression system may include a local wind noise suppression block, a receiver, and a wind noise suppression fusion block. The local wind noise suppression block may be configured to analyze an audio signal and generate, based on the audio signal, local wind noise information associated with a first device comprising the wind noise suppression system, wherein the local wind noise information includes parameters for suppressing local wind noise at the first device. The receiver may be configured to receive remote wind noise information associated with a second device, wherein the remote wind noise information includes parameters for suppressing remote wind noise at the second device. The wind noise suppression fusion block may be configured to combine the local wind noise information and the remote wind noise information into fused wind noise information and apply parameters of the fused wind noise information to modify audio information for playback to an audio transducer of the first device.

Binaural wind noise reduction

An adaptive beamformer includes at least first and second microphones that generate respective audio signals that include speech and noise, a controller that detects occurrences of speech and noise within the audio signals, an adaptive speech cancelling filter that cancels speech from the audio signal of the second microphone to provide a speech-cancelled signal, an adaptive mixing block that combines the speech-cancelled signal and the second microphone audio signal to provide a noise reference signal in a weighted manner such that a weight of the second microphone signal is increased proportionally with an amount of the detected noise and a weight of the speech-cancelled signal is increased proportionally with an amount of the detected speech, and an adaptive noise cancelling filter that uses the noise reference signal to remove the noise from the first microphone audio signal.

System and method for optimized noise reduction in the presence of speech distortion using adaptive microphone array

An apparatus includes a beamformer, an echo suppression control unit, and a residual echo cancellation unit. The beamformer is configured to pass desired portions of audio signals and to suppress undesired portions of the audio signals. The beamformer includes a speech blocking filter to prevent suppression of near-end desired talker speech in the audio signals and an echo suppression filter to suppress echo in the audio signals. An echo suppression control unit is coupled to the beamformer and receives signals and determines whether to dynamically adapt the speech blocking filter or to dynamically adapt the echo suppression filter. The speech blocking filter remains unchanged during dynamic adaptation of the echo suppression filter, and the echo suppression filter remains unchanged during dynamic adaptation of the speech blocking filter. The residual echo cancellation unit is coupled to the beamformer and receives output audio signals from the beamformer and further suppresses residual echo.

Microphone array processing for adaptive echo control

Step size can be used to slow or freeze the adaptive filter to improve AEC system performance, such as during double talk events. An AEC control system may be used to adjust the step size based on an echo-to-disturbance energy ratio (EDER). The algorithm adjusts the step size to lower the adaptation rate when the EDER is small (or the combination of the near signal and noise is large compared to the echo) and raise the adaptation rate when the EDER is large (echo is large compared to a combination of near signal and noise).

Acoustic echo cancellation (AEC) rate adaptation

Acoustic echo cancellation (AEC) processing may be improved by performing echo cancellation using a combined multi-channel reference signal. Two or more reference signals, such as a left and right channel of a stereo source, may be combined and provided to an AEC block configured to receive the combined signal and perform AEC processing using the combined signal. The AEC block may include an adaptive filter that performs operations that cause pre-whitening of the combined reference signal and de-correlation of the individual channels within the combined reference signal. The pre-whitening of the signal flattens the spectrum of the combined reference signal, which may improve convergence speed of the AEC processing in cancelling the echo. The de-correlating of the signal cancels inter-channel correlation between the multiple channels, which may improve convergence speed of the AEC processing in cancelling the echo.

Combined reference signal for acoustic echo cancellation

An acoustic echo cancellation (AEC) system within an audio playback system of an electronic device, such as a mobile phone, may calculate an estimation of an acoustic echo based on parameters describing the transducer reproducing the audio playback signals. Those parameters may include, for example, a resistance and/or inductance of the transducer and a current through and/or a voltage across the transducer. The acoustic echo cancellation system may predict, for example, a coil velocity of the transducer based on the transducer impedance. Then, an echo may be estimated using the predicted coil velocity. That estimated echo may be output to the transducer to cancel echo in the playback signal. Additionally, that estimated echo may be used to predict nonlinearities in the transducer output and an appropriate signal generated to cancel nonlinear behavior.

Nonlinear acoustic echo cancellation based on transducer impedance

A method may include forming a plurality of beams to receive and spatially filter audible sounds from a first microphone and a second microphone. The plurality of beams may include a first unidirectional beam to receive audible sounds from the first microphone and the second microphone, a second unidirectional beam to receive audible sounds from the first microphone and the second microphone, the second unidirectional beam having a spatial null in a direction different from that of the first unidirectional beam, and an omnidirectional beam to receive audible sounds from the first microphone and the second microphone. The method may also include determining a first energy of audible sounds filtered by the first unidirectional beam and a second energy of audible sounds filtered by the second unidirectional beam and, based on the first energy and the second energy, selecting one of the plurality of beams as a selected beam.

Systems and methods for suppressing audio noise in a communication system

A telephone includes a transmit channel and a receive channel, each including a bank of sub-band filters having a VAD coupled one to each sub-band filter. Each VAD measures the spectral energy in a sub-band, compares the spectral energy to a first threshold, and produces an output signal representative of whether or not the first threshold is exceeded. The voice activity detector also includes a threshold circuit for calculating a dynamically adjustable noise threshold based upon averaged measured spectral energy. A wide band or system VAD monitors echo canceling circuitry to detect voice activity and double talk.

Distributed VAD control system for telephone

A background noise estimate based upon a modified Doblinger noise estimate is used for modulating the output of a pseudo-random phase spectrum generator to produce the comfort noise. The circuit for estimating noise includes a smoothing filter having a slower time constant for updating the noise estimate during noise than during speech. Comfort noise is smoothly inserted by basing the amount of comfort noise on the amount of noise suppression. A discrete inverse Fourier transform converts the comfort noise back to the time domain and overlapping windows eliminate artifacts that may have been produced during processing.

Comfort noise generator using modified Doblinger noise estimate

An RF receiver () includes a channel estimator (). The channel estimator () includes a channel estimation filter (), error signal generator (), and normalized Least-Mean-Square (NLMS) filter-definition generator. The filter (), error generator () and NLMS filter-definition generator () function together as an adaptive estimator. An error signal () is formed with a received signal (). The filter () filters an estimated transmit signal () constructed from lower confidence decision data () and higher confidence known preamble data (). An adaptive filter definition () is formed in a feedback, loop which minimizes correlation between the estimated transmit signal () and the error signal (). A convergence signal () is modulated in synchronism with the changes between lower and higher confidence data in the estimated transmit signal ().

RF receiver with NLMS channel estimator and method therefor

Voice activity is detected by comparing a signal with two thresholds and producing data representing the energy of the signal. The data, in binary form, is compared with thresholds to determine voice activity. In accordance with another aspect of the invention, the thresholds are adjusted based upon statistical information. In accordance with another aspect of the invention, the data can be weighted to provide an indication of the quasi-RMS energy of an input signal.

Voice activity detector for telephone

Voice activity is detected by comparing an analog signal with two voltage thresholds and producing data representing the energy of the signal. The data, in binary form, is compared with thresholds to determine voice activity. In accordance with another aspect of the invention, the thresholds are adjusted based upon statistical information. In accordance with another aspect of the invention, the data can be weighted to provide an indication of the quasi-RMS energy of an input signal. The input signal itself is not converted into digital form yet the data derived from the input signal has high resolution.

Analog voice activity detector for telephone

The separation of a adjacent band pass filters is improved, without changing the filters, by inverting the output signals from alternate filters and not inverting the remaining output signals. All the output signals are then summed. The result is a deeper notch in the frequency response of adjacent filters.

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Mesa, AZ, United States of America

Justin Lamar Allen