Abstract: A method of detecting a suitability of a signal for live speech detection, the method comprising: receiving the signal containing speech from a transducer; measuring a signal characteristic of an audible component of the received signal; estimating an expected signal characteristic of an ultrasonic component of the received signal based on the measured signal characteristic of the audible component; determining, based on the estimated expected signal characteristic, whether the ultrasonic component is suitable for detecting whether the speech is live speech.

Abstract: 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.

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 multi-microphone algorithm for detecting and differentiating interference sources from desired talker speech in advanced audio processing for smart home applications is described. The approach is based on characterizing a persistent interference source when sounds repeated occur from a fixed spatial location relative to the device, which is also fixed. Some examples of such interference sources include TV, music system, air-conditioner, washing machine, and dishwasher. Real human talkers, in contrast, are not expected to remain stationary and speak continuously from the same position for a long time. The persistency of an acoustic source is established based on identifying historically-recurring inter-microphone frequency-dependent phase profiles in multiple time periods of the audio data. The detection algorithm can be used with a beamforming processor to suppress the interference and for achieving voice quality and automatic speech recognition rate improvements in smart home applications.

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: 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.

Abstract: The reliable differentiation of human and artificial talkers is important for many automatic speaker verification applications, such as in developing anti-spoofing countermeasures against replay attacks for voice biometric authentication. A multi-microphone approach may exploit small movements of human talkers to differentiate between a human talker and an artificial talker. One method of determining the presence or absence of talker movement includes monitoring the variation of the inter-mic frequency-dependent phase profile of the received microphone array data over a period of time. Using spatial information with spectral-based techniques for determining whether an audio source is a human or artificial talker may reduce the likelihood of success of spoofing attacks against a voice biometric authentication system. The anti-spoofing countermeasure may be used in electronic devices including smart home devices, cellular phones, tablets, and personal computers.

Abstract: A change in the phase pattern of the inter-mic impulse response (IMIR), determined by a cross power spectral density, may be used to detect the appearance of a new talker or a dramatic movement of the current talker. For example, the phase of the IMIR is dependent on a location of the sound source relative to the microphone array. Any signal originating from a specific location has a specific phase pattern on the IMIR across the frequency domain. By comparing phase patterns of the current cross power spectral density with a recorded talker phase profile, a talker change can be detected. This detection can be used to control signal processing algorithms. For example, when talker change is detected, the step size of an adaptive filter can be increased to track the changes efficiently.

Abstract: An apparatus includes a beamformer, an echo suppression control unit, and a residual echo cancellation unit. The beamformer is configured to pass desired portions of audio signals and to suppress undesired portions of the audio signals. The beamformer includes a speech blocking filter to prevent suppression of near-end desired talker speech in the audio signals and an echo suppression filter to suppress echo in the audio signals. An echo suppression control unit is coupled to the beamformer and receives signals and determines whether to dynamically adapt the speech blocking filter or to dynamically adapt the echo suppression filter. The speech blocking filter remains unchanged during dynamic adaptation of the echo suppression filter, and the echo suppression filter remains unchanged during dynamic adaptation of the speech blocking filter. The residual echo cancellation unit is coupled to the beamformer and receives output audio signals from the beamformer and further suppresses residual echo.

Abstract: An apparatus includes a beamformer, an echo suppression control unit, and a residual echo cancellation unit. The beamformer is configured to pass desired portions of audio signals and to suppress undesired portions of the audio signals. The beamformer includes a speech blocking filter to prevent suppression of near-end desired talker speech in the audio signals and an echo suppression filter to suppress echo in the audio signals. An echo suppression control unit is coupled to the beamformer and receives signals and determines whether to dynamically adapt the speech blocking filter or to dynamically adapt the echo suppression filter. The speech blocking filter remains unchanged during dynamic adaptation of the echo suppression filter, and the echo suppression filter remains unchanged during dynamic adaptation of the speech blocking filter. The residual echo cancellation unit is coupled to the beamformer and receives output audio signals from the beamformer and further suppresses residual echo.

Abstract: The reliable differentiation of human and artificial talkers is important for many automatic speaker verification applications, such as in developing anti-spoofing countermeasures against replay attacks for voice biometric authentication. A multi-microphone approach may exploit small movements of human talkers to differentiate between a human talker and an artificial talker. One method of determining the presence or absence of talker movement includes monitoring the variation of the inter-mic frequency-dependent phase profile of the received microphone array data over a period of time. Using spatial information with spectral-based techniques for determining whether an audio source is a human or artificial talker may reduce the likelihood of success of spoofing attacks against a voice biometric authentication system. The anti-spoofing countermeasure may be used in electronic devices including smart home devices, cellular phones, tablets, and personal computers.

Abstract: A change in the phase pattern of the inter-mic impulse response (IMIR), determined by a cross power spectral density, may be used to detect the appearance of a new talker or a dramatic movement of the current talker. For example, the phase of the IMIR is dependent on a location of the sound source relative to the microphone array. Any signal originating from a specific location has a specific phase pattern on the IMIR across the frequency domain. By comparing phase patterns of the current cross power spectral density with a recorded talker phase profile, a talker change can be detected. This detection can be used to control signal processing algorithms. For example, when talker change is detected, the step size of an adaptive filter can be increased to track the changes efficiently.

Abstract: A multi-microphone algorithm for detecting and differentiating interference sources from desired talker speech in advanced audio processing for smart home applications is described. The approach is based on characterizing a persistent interference source when sounds repeated occur from a fixed spatial location relative to the device, which is also fixed. Some examples of such interference sources include TV, music system, air-conditioner, washing machine, and dishwasher. Real human talkers, in contrast, are not expected to remain stationary and speak continuously from the same position for a long time. The persistency of an acoustic source is established based on identifying historically-recurring inter-microphone frequency-dependent phase profiles in multiple time periods of the audio data. The detection algorithm can be used with a beamforming processor to suppress the interference and for achieving voice quality and automatic speech recognition rate improvements in smart home applications.