The present disclosure describes aspects of adaptive energy limiting for transient noise suppression. In some aspects, an adaptive energy limiter sets a limiter ceiling for an audio signal to full scale and receives a portion of the audio signal. For the portion of the audio signal, the adaptive energy limiter determines a maximum amplitude and evaluates the portion with a neural network to provide a voice likelihood estimate. Based on the maximum amplitude and the voice likelihood estimate, the adaptive energy limiter determines that the portion of the audio signal includes noise. In response to determining that the portion of the audio signal includes noise, the adaptive energy limiter decreases the limiter ceiling and provides the limiter ceiling to a limiter module effective to limit an amount of energy of the audio signal. This may be effective to prevent audio signals from carrying full energy transient noise into conference audio.

The present disclosure describes aspects of adaptive energy limiting for transient noise suppression. In some aspects, an adaptive energy limiter sets a limiter ceiling for an audio signal to full scale and receives a portion of the audio signal. For the portion of the audio signal, the adaptive energy limiter determines a maximum amplitude and evaluates the portion with a neural network to provide a voice likelihood estimate. Based on the maximum amplitude and the voice likelihood estimate, the adaptive energy limiter determines that the portion of the audio signal includes noise. In response to determining that the portion of the audio signal includes noise, the adaptive energy limiter decreases the limiter ceiling and provides the limiter ceiling to a limiter module effective to limit an amount of energy of the audio signal. This may be effective to prevent audio signals from carrying full energy transient noise into conference audio.

Within each harmonic spectrum of a sequence of spectra derived from analysis of a waveform representing human speech are identified two or more fundamental or harmonic components that have frequencies that are separated by integer multiples of a fundamental acoustic frequency. The highest harmonic frequency that is also greater than 410 Hz is a primary cap frequency, which is used to select a primary phonetic note that corresponds to a subset of phonetic chords from a set of phonetic chords for which acoustic spectral is available. The spectral data can also include frequencies for primary band, secondary band (or secondary note), basal band, or reduced basal band acoustic components, which can be used to select a phonetic chord from the subset of phonetic chords corresponding to the selected primary note.

Within each harmonic spectrum of a sequence of spectra derived from analysis of a waveform representing human speech are identified two or more fundamental or harmonic components that have frequencies that are separated by integer multiples of a fundamental acoustic frequency. The highest harmonic frequency that is also greater than 410 Hz is a primary cap frequency, which is used to select a primary phonetic note that corresponds to a subset of phonetic chords from a set of phonetic chords for which acoustic spectral is available. The spectral data can also include frequencies for primary band, secondary band (or secondary note), basal band, or reduced basal band acoustic components, which can be used to select a phonetic chord from the subset of phonetic chords corresponding to the selected primary note.

Methods for echo estimation or echo management (echo suppression or cancellation) on an input audio signal, with at least one of adaptation of a sparse prediction filter set, modification (for example, truncation) of adapted prediction filter impulse responses, generation of a composite impulse response from adapted prediction filter impulse responses, or use of echo estimation and/or echo management resources in a manner determined at least in part by classification of the input audio signal as being (or not being) echo free. Other aspects are systems configured to perform any embodiment of any of the methods.

Methods for echo estimation or echo management (echo suppression or cancellation) on an input audio signal, with at least one of adaptation of a sparse prediction filter set, modification (for example, truncation) of adapted prediction filter impulse responses, generation of a composite impulse response from adapted prediction filter impulse responses, or use of echo estimation and/or echo management resources in a manner determined at least in part by classification of the input audio signal as being (or not being) echo free. Other aspects are systems configured to perform any embodiment of any of the methods.

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.

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 vehicle infotainment system that adds background sounds to an outgoing call on a mobile device. The infotainment system comprises: i) a database of selectable augmenting audio signals; and ii) audio processing circuitry configured to receive at a first input an uplink signal from the infotainment system and receive at a second input a selected augmenting audio signal. The audio processing circuitry adapts a spectrum of the first selected augmenting audio signal to prevent the selected augmenting audio signal from masking the uplink signal and combines the adapted selected augmenting audio signal and the uplink signal to produce an augmented uplink signal at an output.

A vehicle infotainment system that adds background sounds to an outgoing call on a mobile device. The infotainment system comprises: i) a database of selectable augmenting audio signals; and ii) audio processing circuitry configured to receive at a first input an uplink signal from the infotainment system and receive at a second input a selected augmenting audio signal. The audio processing circuitry adapts a spectrum of the first selected augmenting audio signal to prevent the selected augmenting audio signal from masking the uplink signal and combines the adapted selected augmenting audio signal and the uplink signal to produce an augmented uplink signal at an output.