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.

Embodiments of the disclosure provide methods and apparatuses processing audio data. The method can include: acquiring audio data by an audio capturing device, determining feature information of an enclosure in which the audio capturing device is located, and reverberating the feature information into the audio data.

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.

A voice assistant includes an electronic processor unit connected to at least one microphone and to remote equipment. The electronic processor unit includes both single detection modules for detecting respective single keywords from an audio signal supplied by the microphone, and also a control unit connected to the single detection modules to select predetermined actions as a function of the detected keywords and to perform those actions. The control module is also arranged to detect whether actions are doable and to activate or deactivate the single detection modules as a function of the doability of the actions.

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.

Provided is an information processing system including: an information processing device (20) and a playback device (10), the information processing device including: a first detection unit (204) that detects, from collected sound, audio processing superimposed on the sound by the playback device; a specifying unit (206) that specifies an utterance subject of the sound on the basis of the audio processing that has been detected; and a determination unit (208) that determines whether or not to execute a command included in the sound on the basis of a result of the specification.

An audio processing system includes at least one first microphone, at least one adaptive filter, and a processor. The at least one first microphone acquires a first audio signal and outputs a first signal based on the first audio signal. The first audio signal includes at least one of a first audio component generated at a first position and a second audio component generated at a second position different from the first position. The first signal is input to the at least one adaptive filter. The at least one adaptive filter outputs a passing signal based on the first signal. The processor, when executing a program stored in a memory, performs: making a determination of which of the first audio component and the second audio component the first audio signal includes more; and controlling a filter coefficient of the adaptive filter based on a result of the determination.

A system for dynamically controlling an echo canceler includes a processor programmed to receive audio data from an audio sensor, to determine a present type of noise in the received audio data, based on the present type of noise, to determine a number of taps to be removed from an echo canceler, and to execute the echo canceler with a reduced number of taps based on the number of taps to be removed.

An estimator of direction of arrival (DOA) of speech from a far-field talker to a device in the presence of room reverberation and directional noise includes audio inputs received from multiple microphones and one or more beamformer outputs generated by processing the microphone inputs. A first DOA estimate is obtained by performing generalized cross-correlation between two or more of the microphone inputs. A second DOA estimate is obtained by performing generalized cross-correlation between one of the one or more beamformer outputs and one or more of: the microphone inputs and other of the one or more beamformer outputs. A selector selects the first or second DOA estimate based on an SNR estimate at the microphone inputs and a noise reduction amount estimate at the beamformer outputs. The SNR and noise reduction estimates may be obtained based on the detection of a keyword spoken by a desired talker.

A method for detecting a prominent tone of an input audio includes establishing a first analysis audio signal based on the input audio signal, establishing a second analysis audio signal based on the input audio signal, wherein an analysis audio signal of the first analysis audio signal and the second analysis audio signal is established by applying an analysis audio filter to the input audio signal, comparing the first analysis audio signal and the second analysis audio signal to obtain an energy level contrast, and determining a representation of the prominent tone by converting the energy level contrast by a contrast-to-frequency mapping function.