Abstract: An adaptive beamformer includes at least first and second microphones that generate respective audio signals that include speech and noise, a controller that detects occurrences of speech and noise within the audio signals, an adaptive speech cancelling filter that cancels speech from the audio signal of the second microphone to provide a speech-cancelled signal, an adaptive mixing block that combines the speech-cancelled signal and the second microphone audio signal to provide a noise reference signal in a weighted manner such that a weight of the second microphone signal is increased proportionally with an amount of the detected noise and a weight of the speech-cancelled signal is increased proportionally with an amount of the detected speech, and an adaptive noise cancelling filter that uses the noise reference signal to remove the noise from the first microphone audio signal.

Abstract: An adaptive beamformer includes at least first and second microphones that generate respective audio signals that include speech and noise, a controller that detects occurrences of speech and noise within the audio signals, an adaptive speech cancelling filter that cancels speech from the audio signal of the second microphone to provide a speech-cancelled signal, an adaptive mixing block that combines the speech-cancelled signal and the second microphone audio signal to provide a noise reference signal in a weighted manner such that a weight of the second microphone signal is increased proportionally with an amount of the detected noise and a weight of the speech-cancelled signal is increased proportionally with an amount of the detected speech, and an adaptive noise cancelling filter that uses the noise reference signal to remove the noise from the first microphone audio signal.

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: 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: Noise sources may be identified as either an interference source, such as a television, or a talker source by analyzing phase information of the microphone signals. A phase delay variance may be computed from pairs of microphone signals. A profile of an interference source may be learned over time by updating a stored profile when the phase delay variance is below a threshold. The stored profile may be used to identify interference sources received by the microphones by determining a correlation between the microphone signals and the stored profile. When an interference source is detected, control parameters may be generated to control a beamformer to reduce contribution of the interference source to an output audio signal. The output audio signal may be used for speech processing, such as in a smart home device.

Abstract: Embodiments are directed towards providing speech enhancement of audio signals from a target source and noise reduction of audio signals from a noise source. A coherence between a first audio signal from a first microphone and a second audio signal from a second microphone may be determined. A first gain function may be determined based on real components of a coherence function, wherein the real components include coefficients based on the previously determined coherence. A second gain function may be determined based on imaginary components of the coherence function. And a third gain function may be determined based on a relationship between a real component of the coherence function and a threshold range. An enhanced audio signal may be generated by applying a combination of the first gain function, the second gain function, and the third gain function to the first audio signal.

Abstract: Embodiments are directed towards providing speech enhancement of audio signals from a target source and noise reduction of audio signals from a noise source. A coherence between a first audio signal from a first microphone and a second audio signal from a second microphone may be determined. A first gain function may be determined based on real components of a coherence function, wherein the real components include coefficients based on the previously determined coherence. A second gain function may be determined based on imaginary components of the coherence function. And a third gain function may be determined based on a relationship between a real component of the coherence function and a threshold range. An enhanced audio signal may be generated by applying a combination of the first gain function, the second gain function, and the third gain function to the first audio signal.