Abstract: An ambient-aware audio system reduces stationary noise and maintains dynamic environmental sound in a received input audio signal. The system includes a signal-to-noise ratio (SNR) estimator that estimates an a priori SNR and an a posteriori SNR, a gain function that uses the estimated SNRs as inputs to compute coefficients of a frequency domain noise reduction filter that uses the computed coefficients to filter a frame of the input audio signal to generate an output audio signal. The SNR estimator, gain function, and filter are configured to iterate over a plurality of frames of the input audio signal. The SNRs are estimated using the input audio signal and the output audio signal associated with one or more of the plurality of frames. The gain function is derived to minimize an expected value of differences between spectral amplitudes of the output audio signal and the input audio signal.

Abstract: A system performs pole-zero or IIR modeling and estimation of an inter-microphone transfer function between first and second microphones that output respective first and second microphone signals. The system includes a first adaptive FIR filter to which the first microphone signal is provided, a delay element that delays the second microphone signal by a predetermined delay amount, and a second adaptive FIR filter to which the delayed second microphone signal is provided. A first coefficient of the second adaptive FIR filter is constrained to a fixed non-zero value. The filters are jointly adapted to minimize an error signal that is a difference of the two filters outputs. The delay is small: approximately the acoustic propagation delay between the two microphones and is not determined by the environmental reverberation characteristics. The error signal may serve as a noise reference in a noise canceller, for implementing far-field beamforming with low delay.

Abstract: A system performs pole-zero or IIR modeling and estimation of an inter-microphone transfer function between first and second microphones that output respective first and second microphone signals. The system includes a first adaptive FIR filter to which the first microphone signal is provided, a delay element that delays the second microphone signal by a predetermined delay amount, and a second adaptive FIR filter to which the delayed second microphone signal is provided. A first coefficient of the second adaptive FIR filter is constrained to a fixed non-zero value. The filters are jointly adapted to minimize an error signal that is a difference of the two filters outputs. The delay is small: approximately the acoustic propagation delay between the two microphones and is not determined by the environmental reverberation characteristics. The error signal may serve as a noise reference in a noise canceller, for implementing far-field beamforming with low delay.

Abstract: 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).

Abstract: Information from microphone signals from a microphone array may be used to identify persistent sources, such as televisions, radios, washing machines, or other stationary sources. Values representative of broadside conditions for each pair of microphone signals are received from the microphone array. By monitoring broadside conditions for microphone pairs, a position of a sound source may be identified. If a sound source is frequently identified with a broadside of the same microphone pair, then that sound source may be identified as a persistent noise source. When a broadside of a pair of microphones is identified with a noise source, a beamformer may be configured to decrease contribution of that pair of microphones to an audio signal formed from the microphone array.

Abstract: Information from microphone signals from a microphone array may be used to identify persistent sources, such as televisions, radios, washing machines, or other stationary sources. Values representative of broadside conditions for each pair of microphone signals are received from the microphone array. By monitoring broadside conditions for microphone pairs, a position of a sound source may be identified. If a sound source is frequently identified with a broadside of the same microphone pair, then that sound source may be identified as a persistent noise source. When a broadside of a pair of microphones is identified with a noise source, a beamformer may be configured to decrease contribution of that pair of microphones to an audio signal formed from the microphone array.

Abstract: 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).

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: In accordance with embodiments of the present disclosure, an audio processing circuit for use in an audio device may perform non-linear acoustic echo cancellation by predicting a displacement associated with an audio speaker, wherein such prediction takes into account a nonlinear response of the audio speaker with a mathematical model that calculates the predicted displacement of the audio speaker as a function of a current signal associated with the audio speaker using a time-varying difference equation, wherein coefficients of the difference equation are based on a set of physical parameters of the audio speaker. From the predicted displacement, the processing circuit may calculate a predicted acoustic output of the audio speaker, which may be used to generate a reference signal to an acoustic echo canceller.

Abstract: In accordance with embodiments of the present disclosure, an audio processing circuit for use in an audio device may perform non-linear acoustic echo cancellation by predicting a displacement associated with an audio speaker, wherein such prediction takes into account a nonlinear response of the audio speaker with a mathematical model that calculates the predicted displacement of the audio speaker as a function of a current signal associated with the audio speaker using a time-varying difference equation, wherein coefficients of the difference equation are based on a set of physical parameters of the audio speaker. From the predicted displacement, the processing circuit may calculate a predicted acoustic output of the audio speaker, which may be used to generate a reference signal to an acoustic echo canceller.

Abstract: A method and apparatus for adaptive precompensation is disclosed. In one embodiment, the method comprises modifying operation of a predistortion filter in response to previous predistorted values and an original input signal, determining a precompensation error between the original input samples and the predicted loudspeaker output, and substantially reducing the precompensation error by using the exact inverse of a loudspeaker model that is a cascaded arrangement of at least one linear system with a non-linear system.

Abstract: A method and apparatus for loudspeaker equalization are described. In one embodiment, the method comprising generating a set of parameters using an invertible, non-linear system based on input audio data and output data corresponding to a prediction of an output of a loudspeaker in response to the input data, and controlling an exact non-linear inverse of the non-linear system using the set of parameters to output a predistorted version of the input data.

Abstract: A method for energy based, non-uniform time-scale compression of audio signals includes receiving a frame of data corresponding to an input audio signal and segmenting the data into a plurality of segments. The method further includes estimating a value related to energy of the frame of data, determining a peak energy estimate for the frame, determining an energy threshold based on the peak energy estimate of the frame and comparing the value related to energy of the frame of the data with the energy threshold to control time-scale compression of the audio data.

Abstract: A method for energy based, non-uniform time-scale compression of audio signals includes receiving a frame of data corresponding to an input audio signal and segmenting the data into a plurality of segments. The method further includes estimating a value related to energy of the frame of data, determining a peak energy estimate for the frame, determining an energy threshold based on the peak energy estimate of the frame and comparing the value related to energy of the frame of the data with the energy threshold to control time-scale compression of the audio data.

Abstract: A method for energy based, non-uniform time-scale compression of audio signals includes receiving a frame of data corresponding to an input audio signal and segmenting the data into a plurality of segments. The method further includes estimating a value related to energy of the frame of data, determining a peak energy estimate for the frame, determining an energy threshold based on the peak energy estimate of the frame and comparing the value related to energy of the frame of the data with the energy threshold to control time-scale compression of the audio data.

Abstract: A coding system and method for a terminal including a multi-rate codec is disclosed. The terminal includes a multi-rate adaptive coder that is capable of transmitting a continuous voice stream transmission at a source code bit rate and a channel code bit rate. A quality of service probing module probes an end-to-end network path of the continuous voice stream transmission to obtain at least one quality of service parameter. A quality of service management module determines at least one constraint associated with the continuous voice stream transmission. An adaptive bit rate algorithm module dynamically adjusts the source code bit rate and the channel code bit rate as a function of the quality of service parameter and the constraint to obtain a maximum value of perceived user performance during the continuous voice stream transmission.

Abstract: An efficient optimization algorithm is provided for multipulse speech coding systems. The efficient algorithm performs computations using the contribution of the non-zero pulses of the excitation function and not the zeroes of the excitation function. Accordingly, efficiency improvements of 87% to 99% are possible with the efficient optimization algorithm.

Abstract: An optimization algorithm is provided for linear prediction based speech coding systems. The optimization algorithm minimizes the error between original speech samples and synthesized speech samples. Optimized linear prediction coefficients are computed directly from a system difference equation without converting the coefficients into the root-domain.