Abstract: Techniques related to a method and system of audio false keyphrase rejection using speaker recognition are described herein. Such techniques use speaker recognition of a computer originated voice to omit actions triggered when a keyphrase is present in captured audio and omitted when speech of the captured audio was spoken by the computer originated voice.

Abstract: Techniques are provided for audio-based detection and tracking of an acoustic source. A methodology implementing the techniques according to an embodiment includes generating acoustic signal spectra from signals provided by a microphone array, and performing beamforming on the acoustic signal spectra to generate beam signal spectra, using time-frequency masks to reduce noise. The method also includes detecting, by a deep neural network (DNN) classifier, an acoustic event, associated with the acoustic source, in the beam signal spectra. The DNN is trained on acoustic features associated with the acoustic event. The method further includes performing pattern extraction, in response to the detection, to identify time-frequency bins of the acoustic signal spectra that are associated with the acoustic event, and estimating a motion direction of the source relative to the array of microphones based on Doppler frequency shift of the acoustic event calculated from the time-frequency bins of the extracted pattern.

Abstract: Techniques related to a method and system of audio false keyphrase rejection using speaker recognition are described herein. Such techniques use speaker recognition of a computer originated voice to omit actions triggered when a keyphrase is present in captured audio and omitted when speech of the captured audio was spoken by the computer originated voice.

Abstract: A computer-implemented method of false keyphrase rejection comprises receiving a captured audio signal of human speech including one or more keyphrases that trigger an action. It also comprises detecting whether or not at least part of the speech is spoken by at least one computer originated voice. The method also has an operation of omitting the triggering of the action at least partly due to the computer originated voice being recognized in the speech.

Abstract: Apparatus, systems, articles of manufacture, and methods to modify sound of speech in an audio signal are disclosed. An example apparatus includes processor circuitry to execute instructions to: identify a first portion of a keyword in the speech of the audio signal during generation of the speech; determine a waveform to replace a second portion of the keyword; and transform the keyword into a different word by introducing the waveform into the audio signal.

Abstract: Techniques are provided for acoustic event detection. A methodology implementing the techniques according to an embodiment includes extracting acoustic features from a received audio signal. The acoustic features may include, for example, one or more short-term Fourier transform frames, or other spectral energy characteristics, of the audio signal. The method also includes applying a trained classifier to the extracted acoustic features to identify and label acoustic event subparts of the audio signal and to generate scores associated with the subparts. The method further includes performing sequence decoding of the acoustic event subparts and associated scores to detect target acoustic events of interest based on the scores and temporal ordering sequence of the event subparts. The classifier is trained on acoustic event subparts that are generated through unsupervised subspace clustering techniques applied to training data that includes target acoustic events.

Abstract: A method, system, and article of multiple task audio analysis uses shared audio processing operations.

Abstract: Techniques related to keyphrase detection for applications such as wake on voice are disclosed herein. Such techniques may have high accuracy by using scores of phone positions in triphones to select which triphones to use with a rejection model, using context-related phones for the rejection model, adding silence before keyphrase sounds for a keyphrase model, or any combination of these.

Abstract: Apparatus, systems, methods, and articles of manufacture are disclosed for acoustic signal processing adaptive to microphone distances. An example system includes a microphone to convert an acoustic signal to an electrical signal and one or more processors to: estimate a distance between a source of the acoustic signal and the microphone; select a signal processing mode based on the distance; and process the electrical signal in accordance with the selected processing mode.

Abstract: A mechanism, method, and computer readable medium to enhance speech enabled devices. The method comprising receiving, by an ultrasonic attack detector of a speech enabled device, an audio stream from one or more microphones and a segmentation signal from a keyword detector indicating a location of a detected keyword within the audio stream, preprocessing, by the ultrasonic attack detector, a segmented portion of the audio stream including the detected keyword to obtain a spectrogram, and executing, by the ultrasonic attack detector, a neural network classifier using the spectrogram as input, the neural network classifier to discern real human speech from intermodulation distortion products resulting from ultrasonic attacks on the speech enabled device.

Abstract: Techniques related to a method and system of audio false keyphrase rejection using speaker recognition are described herein. Such techniques use speaker recognition of a computer originated voice to omit actions triggered when a keyphrase is present in captured audio and omitted when speech of the captured audio was spoken by the computer originated voice.

Abstract: Techniques related to a method and system of audio false keyphrase rejection using speaker recognition are described herein. Such techniques use speaker recognition of a computer originated voice to omit actions triggered when a keyphrase is present in captured audio and omitted when speech of the captured audio was spoken by the computer originated voice.

Abstract: Techniques are provided for efficient acoustic event detection with reduced resource consumption. A methodology implementing the techniques according to an embodiment includes calculating frames of power spectra based on segments of received acoustic signals. The method further includes two processes, one for detecting impulsive acoustic events and another for detecting continuous acoustic events. The first process includes generating impulsive acoustic event features associated with first and second power spectrum frames, applying a neural network classifier to the impulsive acoustic event features to generate event scores, and detecting an impulsive acoustic event based on those event scores.

Abstract: Techniques are provided for audio-based detection and tracking of an acoustic source. A methodology implementing the techniques according to an embodiment includes generating acoustic signal spectra from signals provided by a microphone array, and performing beamforming on the acoustic signal spectra to generate beam signal spectra, using time-frequency masks to reduce noise. The method also includes detecting, by a deep neural network (DNN) classifier, an acoustic event, associated with the acoustic source, in the beam signal spectra. The DNN is trained on acoustic features associated with the acoustic event. The method further includes performing pattern extraction, in response to the detection, to identify time-frequency bins of the acoustic signal spectra that are associated with the acoustic event, and estimating a motion direction of the source relative to the array of microphones based on Doppler frequency shift of the acoustic event calculated from the time-frequency bins of the extracted pattern.

Abstract: An example apparatus for reducing noise in audio includes a preprocessor to receive audio input from a microphone and preprocess the audio input to generate preprocessed audio. The apparatus also includes an acoustic event detector to detect an acoustic event corresponding to a disturbance in the preprocessed audio. The apparatus further includes a noise reduction model selector to select a specific disturbance model based the detected acoustic event. The apparatus further includes a noise suppressor to attenuate components related to the disturbance in the preprocessed audio using the selected specific disturbance model to generate an enhanced audio with suppressed noise.

Abstract: Techniques are provided for pre-processing enhancement of a speech signal. A methodology implementing the techniques according to an embodiment includes performing de-reverberation processing on signals received from an array of microphones, the signals comprising speech and noise. The method also includes generating time-frequency masks (TFMs) for each of the signals. The TFMs indicate the probability that a time-frequency component of the signal associated with that TFM element includes speech. The TFM generation is based on application of a recurrent neural network to the signals. The method further includes generating steering vectors based on speech covariance matrices and noise covariance matrices. The TFMs are employed to filter speech components of the signals, for calculation of the speech covariance, and noise components of the signals for calculation of the noise covariance.

Abstract: A mechanism, method, and computer readable medium to enhance speech enabled devices. The method comprising receiving, by an ultrasonic attack detector of a speech enabled device, an audio stream from one or more microphones and a segmentation signal from a keyword detector indicating a location of a detected keyword within the audio stream, preprocessing, by the ultrasonic attack detector, a segmented portion of the audio stream including the detected keyword to obtain a spectrogram, and executing, by the ultrasonic attack detector, a neural network classifier using the spectrogram as input, the neural network classifier to discern real human speech from intermodulation distortion products resulting from ultrasonic attacks on the speech enabled device.

Abstract: Techniques related to keyphrase detection for applications such as wake on voice are disclosed herein. Such techniques may have high accuracy by using scores of phone positions in triphones to select which triphones to use with a rejection model, using context-related phones for the rejection model, adding silence before keyphrase sounds for a keyphrase model, or any combination of these.

Abstract: Techniques related to a method and system of audio false keyphrase rejection using speaker recognition are described herein. Such techniques use speaker recognition of a computer originated voice to omit actions triggered when a keyphrase is present in captured audio and omitted when speech of the captured audio was spoken by the computer originated voice.

Abstract: Techniques are provided for pre-processing enhancement of a speech signal. A methodology implementing the techniques according to an embodiment includes performing de-reverberation processing on signals received from an array of microphones, the signals comprising speech and noise. The method also includes generating time-frequency masks (TFMs) for each of the signals. The TFMs indicate the probability that a time-frequency component of the signal associated with that TFM element includes speech. The TFM generation is based on application of a recurrent neural network to the signals. The method further includes generating steering vectors based on speech covariance matrices and noise covariance matrices. The TFMs are employed to filter speech components of the signals, for calculation of the speech covariance, and noise components of the signals for calculation of the noise covariance.