Abstract: A system suppresses howl in a device including microphones and speakers, for example, an artificial reality headset. A speaker of the device presents audio content. The audio content presented by the speaker is received by a microphone of the device thereby creating a howl in certain situations. The system detects the presence of the howl in a region of the audio content using an adaptive notch filter. The system suppresses the howl by reducing gain of one or more frequencies of the audio content. The system may detect presence of the howl by monitoring flatness of the signal.

Abstract: An audio system for artificial reality systems is configured to reduce feedback and mitigate entrainment in audio content presented to the user. The audio system utilizes adaptive feedback cancellation processes to reduce feedback. The audio system processes reference signals in a hybrid of the time domain and the frequency domain to achieve rapid convergence with decreased entrainment. The audio system may pre-whiten reference signals and speaker signals and separate the pre-whitened signals into frequency bands using a filter bank. The audio system uses a state space algorithm to generate adaptive filters in each frequency band. The adaptive filters may be applied to the speaker signal to generate a feedback cancelation signal which may be subtracted from the reference signal to decrease feedback.

Abstract: An audio system for artificial reality systems is configured to reduce feedback and mitigate entrainment in audio content presented to the user. The audio system utilizes adaptive feedback cancellation processes to reduce feedback. The audio system processes reference signals in a hybrid of the time domain and the frequency domain to achieve rapid convergence with decreased entrainment. The audio system may pre-whiten reference signals and speaker signals and separate the pre-whitened signals into frequency bands using a filter bank. The audio system uses a state space algorithm to generate adaptive filters in each frequency band. The adaptive filters may be applied to the speaker signal to generate a feedback cancelation signal which may be subtracted from the reference signal to decrease feedback.

Abstract: Systems and processes for operating an intelligent automated assistant are provided. An examples process of operating an intelligent automated assistant includes, at an electronic device with one or more processors and memory, receiving audio input, determining a direct-to-reverberant energy ratio based on the audio input, and determining a head pose of a user based on the direct-to-reverberant energy ratio.

Abstract: Systems and processes for operating an intelligent automated assistant are provided. An examples process of operating an intelligent automated assistant includes, at an electronic device with one or more processors and memory, receiving audio input, determining a direct-to-reverberant energy ratio based on the audio input, and determining a head pose of a user based on the direct-to-reverberant energy ratio.

Abstract: An echo canceller is disclosed in which audio signals of the playback content received by one or more of the microphones from a loudspeaker of the device may be used as the playback reference signals to estimate the echo signals of the playback content received by a target microphone for echo cancellation. The echo canceller may estimate the transfer function between a reference microphone and the target microphone based on the playback reference signal of the reference microphone and the signal of the target microphone. To mitigate near-end speech cancellation at the target microphone, the echo canceller may compute a mask to distinguish between target microphone audio signals that are echo-signal dominant and near-end speech dominant. The echo canceller may use the mask to adaptively update the transfer function or to modify the playback reference signal used by the transfer function to estimate the echo signals of the playback content.

Abstract: An echo canceller is disclosed in which audio signals of the playback content received by one or more of the microphones from a loudspeaker of the device may be used as the playback reference signals to estimate the echo signals of the playback content received by a target microphone for echo cancellation. The echo canceller may estimate the transfer function between a reference microphone and the target microphone based on the playback reference signal of the reference microphone and the signal of the target microphone. To mitigate near-end speech cancellation at the target microphone, the echo canceller may compute a mask to distinguish between target microphone audio signals that are echo-signal dominant and near-end speech dominant. The echo canceller may use the mask to adaptively update the transfer function or to modify the playback reference signal used by the transfer function to estimate the echo signals of the playback content.

Abstract: Audio signals produced by microphones can be processed to remove echo and reverberation. The processed signals can be mapped to each other with adaptively estimated impulse responses. One or more of the processed signals, one or more of the mapped signals, and one or more of the impulse responses can be fed to an automatic speech recognizer (ASR) having a deep neural network (DNN), to train the DNN or recognize speech in the input audio signals. Other aspects are described and claimed.

Abstract: Method for echo control using adaptive polynomial filters in sub-band domain starts with loudspeaker that is configured to be driven by a reference signal outputting a loudspeaker signal. Microphone receives at least one of: a near-end speaker signal, ambient noise signal, or the loudspeaker signal and generates a microphone signal. Adaptive polynomial filters in sub-band domain included in adaptive echo canceller (AEC) are configured to adaptively filter representation of the reference signal in a plurality of channels in a sub-band domain based on a clean signal to generate the echo estimate. Echo suppressor is configured to remove an echo estimate from the microphone signal to generate the clean signal. Other embodiments are described.

Abstract: Method of speech enhancement using Neural Network-based combined signal starts with training neural network offline which includes: (i) exciting at least one accelerometer and at least one microphone using training accelerometer signal and training acoustic signal, respectively. The training accelerometer signal and the training acoustic signal are correlated during clean speech segments. Training neural network offline further includes (ii) selecting speech included in the training accelerometer signal and in the training acoustic signal, and (iii) spatially localizing the speech by setting a weight parameter in the neural network based on the selected speech included in the training accelerometer signal and in the training acoustic signal. The neural network that is trained offline is then used to generate a speech reference signal based on an accelerometer signal from the at least one accelerometer and an acoustic signal received from the at least one microphone. Other embodiments are described.

Abstract: Method of speech enhancement using Neural Network-based combined signal starts with training neural network offline which includes: (i) exciting at least one accelerometer and at least one microphone using training accelerometer signal and training acoustic signal, respectively. The training accelerometer signal and the training acoustic signal are correlated during clean speech segments. Training neural network offline further includes (ii) selecting speech included in the training accelerometer signal and in the training acoustic signal, and (iii) spatially localizing the speech by setting a weight parameter in the neural network based on the selected speech included in the training accelerometer signal and in the training acoustic signal. The neural network that is trained offline is then used to generate a speech reference signal based on an accelerometer signal from the at least one accelerometer and an acoustic signal received from the at least one microphone. Other embodiments are described.

Abstract: Apparatus for linear and nonlinear acoustic echo control includes loudspeaker, first, second, and third microphone, beamformer, and first echo canceller. The loudspeaker outputs a loudspeaker signal that includes reference signal. The first microphone and the second microphone are collocated with the loudspeaker, receive at least one of: a near-end speaker signal from a near-end speaker and the loudspeaker signal, and generate first and second microphone uplink signals, respectively. The third microphone receives the near-end speaker signal and generates a third microphone uplink signal. The beamformer receives the first and second microphone uplink signals, directs a beam towards the loudspeaker and drives a null towards the near-end speaker, and generates a beamformer output. The first echo canceler receives the third microphone uplink signal and the beamformer output, and cancels echoes in the third microphone uplink signal based on the beamformer output to generate an echo cancelled signal.