Abstract: Methods and apparatus to audio adjustment based on vocal effort are disclosed herein. An example apparatus comprising interface circuitry, machine readable instructions, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to identify speech with a soft voice type in audio from a first user device, the speech with the soft voice type including phonation, modify the audio to generate modified audio based on the identification of the speech with the soft voice type, and output the modified audio from a second user device.

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 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: An example apparatus for suppression of key phrase detection includes an audio receiver to receive generated audio from a loopback endpoint and captured audio from a microphone. The apparatus includes a self-trigger detector to detect a key phrase in the generated audio. The apparatus also includes a detection suppressor to suppress detection of the detected key phrase in the captured audio at a second key detector for a predetermined time in response to detecting the key phrase in the generated audio.

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 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 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: An example apparatus for suppression of key phrase detection includes an audio receiver to receive generated audio from a loopback endpoint and captured audio from a microphone. The apparatus includes a self-trigger detector to detect a key phrase in the generated audio. The apparatus also includes a detection suppressor to suppress detection of the detected key phrase in the captured audio at a second key detector for a predetermined time in response to detecting the key phrase in the generated audio.

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: System and techniques for a microphone board for far field automatic speech recognition are described herein. The microphone board may include a first plurality of microphones disposed along a circumference of a circle on a surface and a second plurality of microphones disposed along a line on the surface. First connections to the first plurality of microphones may be grouped together and second connections to the second plurality of microphones are grouped together. The first connections and the second connections may be provided to an external entity of the surface via a connector.

Abstract: System and techniques for automatic speech recognition de-reverberation are described herein. A portion of an audio stream may be obtained. here, the portion of the audio stream is a proper subset of the audio stream. A filter may be created by applying Generalized Weighted Prediction Error (GWPE) to the portion of the audio stream. The filter may be applied to the audio stream to remove reverberation. The filtered version of the audio stream may then be provided to an audio stream consumer.

Abstract: System and techniques for automatic speech recognition pre-processing are described herein. First, a plurality of audio channels may be obtained. Then, reverberations mat be removed from the audio channels. The plurality of audio channels may be partitioned into beams after reverberations are removed. A partition corresponding to a beam in the beams may be selected based on a noise level. An audio signal may be filtered from the selected partition. The filtered audio signal may be provided to an external entity via an output interface of the pre-processing pipeline.

Abstract: Audio beam forming control is described herein. A system may include a camera, a plurality of microphones, a memory, and a processor. The memory is to store instructions and that is communicatively coupled to the camera and the plurality of microphones. The processor is communicatively coupled to the camera, the plurality of microphones, and the memory. When the processor is to execute the instructions, the processor is to capture a video stream from the camera, determine, from the video stream, an audio source position, capture audio from the primary audio source position at a first direction, and attenuate audio originating from other than the first direction.

Abstract: Embodiments of a system and method for adapting a phase difference-based noise reduction system are generally described herein. In some embodiments, spatial information associated with a first and second audio signal are determined, wherein the first and second audio signals including a target audio inside a beam and noise from outside the beam. A signal-to-noise ratio (SNR) associated with the audio signals is estimated. A mapping of phase differences to gain factors is adapted for determination of attenuation factors for attenuating frequency bins associated with noise outside the beam. Spectral subtraction is performed to remove estimated noise from the single-channel signal based on a weighting that affects frequencies associated with a target signal less. Frequency dependent attenuation factors are applied to attenuate frequency bins outside the beam to produce a target signal having noise reduced.

Abstract: Embodiments of a system and method for adapting a phase difference-based noise reduction system are generally described herein. In some embodiments, spatial information associated with a first and second audio signal are determined, wherein the first and second audio signals including a target audio inside a beam and noise from outside the beam. A signal-to-noise ratio (SNR) associated with the audio signals is estimated. A mapping of phase differences to gain factors is adapted for determination of attenuation factors for attenuating frequency bins associated with noise outside the beam. Spectral subtraction is performed to remove estimated noise from the single-channel signal based on a weighting that affects frequencies associated with a target signal less. Frequency dependent attenuation factors are applied to attenuate frequency bins outside the beam to produce a target signal having noise reduced.