Abstract: Techniques are provided for detection of laser-based audio injection attacks. A methodology implementing the techniques according to an embodiment includes calculating cross correlations between signals received from microphones of an array of two or more microphones. The method also includes identifying time delays associated with peaks of the cross correlations, and magnitudes associated with the peaks of the cross correlations. The method further includes calculating a time alignment metric based on the time delays and calculating a similarity metric based on the magnitudes. The method further includes generating a first attack indicator based on a comparison of the time alignment metric to a first threshold and generating a second attack indicator based on a comparison of the similarity metric to a second threshold. The method further includes providing warning of a laser-based audio attack based on the first attack indicator and/or the second attack indicator.

Abstract: Techniques are provided for audio capture path evaluation of microphones incorporated into a device under test (DUT). A methodology implementing the techniques according to an embodiment includes estimating impulse responses (IRs) of the DUT microphones based on a comparison of a test audio signal received through the DUT microphones, at a selected measurement angle, to the test audio signal received through a reference microphone. The method also includes calculating group delays for the DUT microphones based on phase responses of the estimated IRs and calculating an average of the group delays. The method further includes calculating a distance, projected onto the measurement angle, between the DUT microphones and a geometric center of the DUT microphones. The distance is calculated as a product of the speed of sound and a difference between the average delay and the group delays for the DUT microphones. The process is repeated for additional measurement angles.

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: A method of audio device performance testing generates virtual audio device data packages.

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: In an embodiment, a system includes a first sensor sensing brain signal data from a user, the brain signal data including nerve signals transmitted via a brain of the user and corresponding to a first set of words spoken by the user. The system also includes a second sensor sensing audio data from the user corresponding to the first set of words and one or more processors communicatively coupled to the first sensor and the second sensor. In the embodiment, the one or more processors generate text data based on the audio data using a machine learning algorithm and re-train the machine learning algorithm based on the brain signal data and the text data to generate a re-trained machine learning algorithm, wherein the re-trained machine learning algorithm generates second text data associated with a second set of words based on second brain signal data.

Abstract: Techniques are provided for audio capture path evaluation of microphones incorporated into a device under test (DUT). A methodology implementing the techniques according to an embodiment includes estimating impulse responses (IRs) of the DUT microphones based on a comparison of a test audio signal received through the DUT microphones, at a selected measurement angle, to the test audio signal received through a reference microphone. The method also includes calculating group delays for the DUT microphones based on phase responses of the estimated IRs and calculating an average of the group delays. The method further includes calculating a distance, projected onto the measurement angle, between the DUT microphones and a geometric center of the DUT microphones. The distance is calculated as a product of the speed of sound and a difference between the average delay and the group delays for the DUT microphones. The process is repeated for additional measurement angles.

Abstract: Techniques are provided for detection of laser-based audio injection attacks. A methodology implementing the techniques according to an embodiment includes calculating cross correlations between signals received from microphones of an array of two or more microphones. The method also includes identifying time delays associated with peaks of the cross correlations, and magnitudes associated with the peaks of the cross correlations. The method further includes calculating a time alignment metric based on the time delays and calculating a similarity metric based on the magnitudes. The method further includes generating a first attack indicator based on a comparison of the time alignment metric to a first threshold and generating a second attack indicator based on a comparison of the similarity metric to a second threshold. The method further includes providing warning of a laser-based audio attack based on the first attack indicator and/or the second attack indicator.

Abstract: Techniques are provided for detection of laser-based audio injection attacks through classification of the acoustic environment. A methodology implementing the techniques according to an embodiment includes broadcasting a reference signal over a loudspeaker into a local environment, and generating a reference model of the local environment based on analysis of a transformed version of that reference signal received through a microphone of the device. The method further includes generating an estimate model based on analysis of a segment of speech in an audio signal received through the microphone. The estimate model is associated with an environment in which the speech was generated. The method further includes calculating a similarity metric (e.g., mathematical distance) between the reference model and the estimate model, and providing warning of a laser-based audio attack if the similarity metric exceeds a threshold value associated with an attack.

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: For a multiple microphone system, a phase response mismatch may be corrected. One embodiment includes receiving audio from a first microphone and from a second microphone, the microphones being coupled to a single device for combining the received audio, recording the received audio from the first microphone and the second microphone before combining the received audio, detecting a phase response mismatch in the recording at the device between the audio received at the second microphone and the audio received at the first microphone, if a phase response mismatch is detected, then estimating a phase delay between the second microphone and the first microphone, and storing the estimated phase delay for use in correcting the phase delay in received audio before combining the received audio.

Abstract: A mechanism is described for facilitating automatic gain adjustment in audio systems according to one embodiment. A method of embodiments, as described herein, includes determining status of one or more of gain settings, mute settings, and boost settings associated with one or more microphones based on a configuration of a computing device including a voice-enabled device. The method may further comprise recommending adjustment of microphone gain based on the configuration and the status of one or more of the gain, mute, and boost settings, and applying the recommended adjustment of the microphone gain.

Abstract: System and techniques for automatic gain control for speech recognition are described herein. An audio signal may be obtained. A signal-to-noise ratio (SNR) may be derived from the audio signal. The SNR may be compared to a threshold. A stored gain value may be updated when the SNR is beyond the threshold and the stored gain value may be applied to a descendant (e.g., later) of the audio signal otherwise.

Abstract: Techniques are provided for defending against an ultrasonic attack on a speech enabled device. A methodology implementing the techniques according to an embodiment includes detecting voice activity in an audio signal received by the device and generating an ultrasonic jamming signal in response to the detection. The jamming signal is broadcast over a loudspeaker for up to the duration of the detected voice activity to defend against the ultrasonic attack. According to another embodiment, the ultrasonic jamming signal is generated in response to detection of a wake-on-voice key phrase in the received audio signal, and the jamming signal is broadcast over the loudspeaker for a time duration selected to be less than or equal to a time window during which spoken commands are accepted by the device following the wake-on-voice key phrase detection. The jamming signal may include white or colored noise, combinations of tones, and/or a periodic sweep frequency.

Abstract: A mechanism is described for facilitating multi-device reverberation estimation according to one embodiment. An apparatus of embodiments, as described herein, includes detection and capture logic to facilitate a microphone of a first voice-enabled device of multiple voice-enabled devices to detect a command from a user. The apparatus further includes calculation logic to facilitate a second voice-enabled device and a third voice-enabled device to calculate speech to reverberation modulation energy ratio (SRMR) values based on the command, where the calculation logic us further to estimate reverberation times (RTs) based on the SRMR values. The apparatus further includes decision and application logic to perform dereverberation based on the estimated RTs of the reverberations.

Abstract: A mechanism is described for facilitating simultaneous recognition and processing of multiple speeches from multiple users according to one embodiment. A method of embodiments, as described herein, includes facilitating a first microphone to detect a first speech from a first speaker, and a second microphone to detect a second speech from a second speaker. The method may further include facilitating a first beam-former to receive and process the first speech, and a second beam-former to receive and process the second speech, where the first and second speeches are at least received or processed simultaneously. The method may further include communicating a first output associated with the first speech and a second output associated with the second speech to the first speaker and the second speaker, respectively, using at least one of one or more speaker devices and one or more display devices.

Abstract: A mechanism is described for facilitating multi-modal dereverberation in far-field audio systems according to one embodiment. A method of embodiments, as described herein, includes performing geometry estimation of a geographical space based on visuals of the space received from one or more cameras of a computing device. The method may further include computing reverberation time based on the geometry estimation that is further based on the visuals, and computing and applying dereverberation based on the reverberation time.

Abstract: A mechanism is described for facilitating context-based cancellation and amplification of acoustical signals in acoustical environments according to one embodiment. An apparatus of embodiments, as described herein, includes detection and recognition logic to detect an acoustical signal being emitted by an acoustical signal source; evaluation, estimation, and footprint logic to classify the acoustical signal as an emergency acoustical signal or a non-emergency acoustical signal, wherein the classification is based on a footprint or a footprint identification (ID) associated with the acoustical signal; acoustical signal cancellation logic to cancel the acoustical signal if the acoustical signal is regarded as the non-emergency acoustical signal based on the footprint or the footprint ID; and acoustical signal amplification logic to amplify the acoustical signal if the acoustical signal is classified as the emergency acoustical signal based on the footprint or the footprint ID.

Abstract: A mechanism is described for facilitating simultaneous recognition and processing of multiple speeches from multiple users according to one embodiment. A method of embodiments, as described herein, includes facilitating a first microphone to detect a first speech from a first speaker, and a second microphone to detect a second speech from a second speaker. The method may further include facilitating a first beam-former to receive and process the first speech, and a second beam-former to receive and process the second speech, where the first and second speeches are at least received or processed simultaneously. The method may further include communicating a first output associated with the first speech and a second output associated with the second speech to the first speaker and the second speaker, respectively, using at least one of one or more speaker devices and one or more display devices.

Abstract: A mechanism is described for facilitating multi-device reverberation estimation according to one embodiment. An apparatus of embodiments, as described herein, includes detection and capture logic to facilitate a microphone of a first voice-enabled device of multiple voice-enabled devices to detect a command from a user. The apparatus further includes calculation logic to facilitate a second voice-enabled device and a third voice-enabled device to calculate speech to reverberation modulation energy ratio (SRMR) values based on the command, where the calculation logic us further to estimate reverberation times (RTs) based on the SRMR values. The apparatus further includes decision and application logic to perform dereverberation based on the estimated RTs of the reverberations.