Microphone signal is received from at least one microphone. AEC produces an echo cancelled microphone signal using first adaptive filters to estimate and cancel feedback that is a result of the environment. AFC produces a processed microphone signal using second adaptive filters to estimate and cancel feedback resulting from application of the reinforced voice signal within the environment. The uttered speech is reinforced in the processed microphone signal to produce the reinforced voice signal. The reinforced voice signal and the audio signal is applied to the loudspeakers. A step size of adjustment of the second adaptive filters may be increased responsive to detection of reverberation in the microphone signal. The reverberation that is used to control the step size of the second adaptive filters may be added artificially. This may provide multiple benefits including improving adjustment of the second adaptive filters and also improving the sound impression of the voice.

A method includes receiving a microphone audio signal and a playout audio signal, and determining a frequency representation of the microphone audio signal and a frequency representation of the playout audio signal. For each frequency representation, the method also includes determining features based on the frequency representation. Each feature corresponds to a pair of frequencies of the frequency representation and a period of time between the pair of frequencies. The method also includes determining that a match occurs between a first feature based on the frequency representation of the microphone audio signal and a second feature based on the frequency representation of the playout audio signal, and determining that a delay value between the first feature and the second feature corresponds to an echo within the microphone audio signal.

A system and method are provided for dynamic sound mask adjustment. A sound mask is used for obtaining an impulse response measurement that adjusts a generated sound mask dynamically based on real-time ambient noise parameters, while maintaining echo canceller calibration performance. The system includes a dynamic sound mask generator that includes a noise accumulator and monitor that includes a processor and memory including instructions executed by the processor for performing the dynamic sound mask adjustment. If the sound mask is not in the hysteresis range, the current sound mask level and iteration update rate are adjusted. if the sound mask is in the hysteresis range, the current sound mask level and iteration update rate are maintained.

A playback device is configured to receive, via a network interface, a source stream of audio including first and second channel streams of audio, and to produce, via respective first and second speaker drivers, a first channel audio output and a second channel audio output. The playback device is also configured to receive, via one or more microphones, a captured stream of audio including first and second portions corresponding to the respective first and second channel audio outputs. The playback device is also configured to combine at least the first channel stream of audio and the second channel stream of audio into a compound audio signal and perform acoustic echo cancellation on the compound audio signal and thereby produce an acoustic echo cancellation output, then to apply the acoustic echo cancellation output to the captured stream of audio and thereby increase a signal-to noise ratio of the captured stream of audio.

A multi-channel acoustic echo cancellation (AEC) system that includes a step-size controller that dynamically determines a step-size value for each channel and each tone index on a frame-by-frame basis. The system determines that near-end signals are present by calculating a scaled error and determining that the scaled error exceeds a threshold value. When the scaled error exceeds the threshold value, the system may switch from a first cost function to a second cost function and determine a step-size value using a robust algorithm. The robust algorithm may prevent the system from diverging due to the presence of the near-end signal. For example, the robust algorithm may select a different cost function to determine the step-size value and/or combine different step-size computations, resulting in the step-size value being temporarily reduced. Thus, the robust algorithm may enable the AEC to better model the near-end disturbance statistics while the near-end signal is present.

Some implementations involve receiving a content stream that includes audio data, determining a content type corresponding to the content stream and determining, based at least in part on the Receiving, by a control system and via an interface system, a content stream that includes audio data content type, a noise compensation method. Some examples involve performing the noise compensation method on the audio data to produce noise-compensated audio data, rendering the noise-compensated audio data for reproduction via a set of audio reproduction transducers of the audio environment, to produce rendered audio signals, and providing the rendered audio signals to at least some audio reproduction transducers of the audio environment.

An audio system for a seat headrest includes N speaker(s), each configured to be integrated into a seat headrest, N being an integer greater than or equal to 1, and an audio processing device including an electronic transmission channel connected to the speaker(s) and configured to transmit at least one audio stream via the speaker(s). The audio system comprises P microphone(s), each configured to be integrated into the headrest, P being an integer greater than or equal to 1. The audio processing device further includes an electronic receiving channel connected to the microphone(s) and configured to receive at least one sound signal via the P microphone(s).

A method for performing echo cancellation includes: receiving a far-end signal from a far-end device at a near-end device; recording a microphone signal at the near-end device including: a near-end signal; and an echo signal corresponding to the far-end signal; extracting far-end features from the far-end signal; extracting microphone features from the microphone signal; computing estimated near-end features by supplying the microphone features and the far-end features to an acoustic echo cancellation module including: an echo estimator including a first stack of a recurrent neural network configured to compute estimated echo features based on the far-end features; and a near-end estimator including a second stack of the recurrent neural network configured to compute the estimated near-end features based on an output of the first stack and the microphone signal; computing an estimated near-end signal from the estimated near-end features; and transmitting the estimated near-end signal to the far-end device.

Embodiments include a processing device communicatively coupled to a plurality of audio devices comprising at least one microphone and at least one speaker, and to a digital signal processing (DSP) component having a plurality of audio input channels for receiving audio signals captured by the at least one microphone, the processing device being configured to identify one or more of the audio devices based on a unique identifier associated with each of said one or more audio devices; obtain device information from each identified audio device; and adjust one or more settings of the DSP component based on the device information. A computer-implemented method of automatically configuring an audio conferencing system, comprising a digital signal processing (DSP) component and a plurality of audio devices including at least one speaker and at least one microphone, is also provided.

A method for preparing reference signals for an echo cancellation system disposed in a vehicle, comprising the steps of: receiving a plurality of drive signals, each drive signal being provided to an associated transducer of a plurality of acoustic transducers such that the associated acoustic transducer transduces the drive signal into an acoustic signal, filtering each drive signal with a respective filter of a plurality of filters to produce a plurality of filtered signals, wherein each of the plurality of filters approximates a transfer function from an associated acoustic transducer to a microphone disposed within the vehicle such that the plurality of filtered signals each estimate a respective acoustic signal at the microphone; summing together at least a subset of the plurality of filtered signals to produce a summed reference signal; and outputting the summed reference signal to an echo cancellation system.