Abstract: Voice anonymization systems and methods are provided. Voice anonymization is done on the speaker's computing device and can prevent voice theft. The voice anonymization systems and methods are lightweight and run efficiently in real time on a computing device, allowing for speaker anonymity without diminishing system performance during a teleconference or VoIP meeting. The anonymization system outputs a transformed speaker voice. The anonymization system can also generate a voice embedding that can be used to reconstruct the original speaker voice. The voice embedding can be encrypted and transmitted to another device. Sometimes, the voice embedding is not transmitted and the listener receives the anonymized voice. Systems and methods are provided for the detection of voice transformations in received audio. Thus, a listener can be informed whether the speaker voice output from the listener's computing device is the original speaker's voice or a transformed version of the original speaker voice.

Abstract: A method and system of neural network dynamic noise suppression (DNS) is provided for audio processing. The system is a down-scaled DNS model that uses grouping techniques at pointwise convolutional layers to reduce the number of network parameters. According to one technique, audio signal data can be coded into an input vector that that is split into multiple groups, each groups having multiple channels. At a pointwise convolution layer, an output is generated for each group. The outputs can be concatenated to form a single input vector for a next layer of the model. Each group is treated as a channel, such that the reduction in the number of channels reduces the number of parameters used by the neural network. In some examples, the groups are weight sharing groups.

Abstract: A method and system of neural network dynamic noise suppression is provided for audio processing.

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 system, method and computer readable medium for dynamic noise reduction in a voice call. The system includes an encoder having a short-time Fourier transform module to determine a magnitude spectrum and a phase spectrum of an input audio signal. The input audio signal includes speech and dynamic noise. A separator is coupled to the encoder. The separator comprises a temporal convolution network (TCN) used to develop a separation mask using the magnitude spectrum as input. The TCN is trained using a frequency SNR function used to calculate loss during training. A mixer is coupled to the separator to multiply the separation mask with the magnitude spectrum to separate the speech from the dynamic noise to obtain a denoise magnitude spectrum. The system also includes a decoder coupled to the mixer and the encoder. The decoder includes an inverse short-time Fourier transform module to reconstruct the input audio signal without the dynamic noise using the denoise magnitude spectrum and the phase spectrum.

Abstract: An example apparatus for reducing to reduce 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 on 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 enhanced audio with suppressed noise.

Abstract: A system, article, device, apparatus, and method for a multi-microphone audio signal unifier comprises receiving, by processor circuitry, an initial audio signal from one of multiple microphones arranged to provide the initial audio signal. This also includes modifying the initial audio signal comprising using at least one neural network (NN) to generate a unified audio signal that is more generic to a type of microphone than the initial audio signal.

Abstract: A computer-implemented method of audio processing comprises receiving, by at least one processor, multiple audio signals from multiple microphones. The audio signals are associated with audio emitted from a same source. The method also may include determining an audio quality indicator of individual ones of the audio signals using a neural network, and selecting at least one of the audio signals depending on the audio quality indicators.

Abstract: Techniques are provided herein for auto-muting procedures that result in efficient high-quality audio capture in a multi-device environment. In particular, when there are multiple computing devices in a shared meeting room, the microphone with the highest rated audio input is selected for the teleconference audio input from the shared environment. Each computing device connected to the teleconference from the meeting room determines a score for its microphone signal. The score is shared with the other devices in the room, and the microphone signal with the highest score is transmitted to the conference. Host-based systems include a host device receiving and reviewing the scores and determining which microphones to auto-mute. Other distributed systems include each computing device transmitting its score to the other devices and receiving the scores from the other devices, and each device determining whether to auto-mute.

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: Methods, apparatus, systems, and articles of manufacture are disclosed. An example apparatus includes: interface circuitry; instructions; and programmable circuitry to at least one of execute or instantiate the instructions to: calculate a sample embedding vector that characterizes a speaker based on a first audio signal; perform a first update of a personal embedding vector based on the sample embedding vector, the updated personal embedding vector to characterize the speaker based on a second audio signal and the first audio signal, and perform a second update of the personal embedding vector based on the first update and a universal embedding vector.

Abstract: A system, method and computer readable medium for dynamic noise reduction in a voice call. The system includes an encoder having a short-time Fourier transform module to determine a magnitude spectrum and a phase spectrum of an input audio signal, including speech and dynamic noise. A separator coupled to the encoder comprises a temporal convolution network (TCN) used to develop a separation mask using the magnitude spectrum as input. The TCN is trained using a frequency SNR function used to calculate loss during training. A mixer is coupled to the separator to multiply the separation mask with the magnitude spectrum to separate the speech from the dynamic noise to obtain a denoise magnitude spectrum. A decoder coupled to the mixer and the encoder includes an inverse short-time Fourier transform module to reconstruct the input audio signal without the dynamic noise using the denoise magnitude spectrum and the phase spectrum.

Abstract: Techniques are provided herein for implementing headset control functions for low-end consumer-grade headsets using a firmware module implemented in the computing platform. The techniques can include a microphone mute function, a headset speaker volume function, and other headset functions. In particular, acoustic events are utilized to control headset functions. Because the headset control components are inside system firmware, the headset control module is endpoint agnostic and will work with any headset coupled to the computing platform. The computing device through which a voice call is implemented can include an event trigger detector, which detects selected acoustic events and triggers a corresponding action. The system allows for control of the voice call via custom user acoustic events. In some examples, the acoustic event that mutes the microphone can be a finger tap. A finger tap generally has a short duration and is easily detectable.

Abstract: A user computing device includes a microphone to generate an audio signal and a self-noise silencer to generate a feature set corresponding to the audio signal, where the input feature identifies, for each of a plurality of frequency components in the audio signal, a respective magnitude value. At least a portion of the feature set is provided as an input to a machine learning model trained to infer frequencies contributing to self-noise generated at the microphone. An attenuation mask is generated, based on an output of the machine learning model, that identifies an attenuation value for at least a subset of the plurality of frequency components. The attenuation mask is applied to at least the subset of the magnitude values of the plurality of frequency components to remove self-noise from the audio signal and generate a denoised version of the audio signal.

Abstract: This disclosure describes systems, methods, and devices related to noise suppression processing. A device may establish a connection to a connected device. The device may calculate a first SNR of a first sample of a first audio stream from the connected device. The device may calculate a second SNR of a second sample of a second audio stream from a first device. The device may compare a difference of the first SNR and the second SNR to a Delta SNR threshold. The device may detect ambient noise level conditions and the Delta SNR threshold is based on the ambient noise level conditions. The device may determine whether to apply system-level preprocessing based on the comparison.

Abstract: Low latency neural network models are provided that can be used for speech processing. The neural networks allow for real-time inference of CNN models without an increase in computer complexity or memory footprint. Buffers are used for upsampling, and the depth of the convolutions varies by frame number. In some examples, a condition is applied within the convolution block to determine a depth of convolutions based on the frame number. In some examples, the network includes multiple convolution sub-model blocks, each having a different depth, and a table is used to select the convolution sub-model block for each frame based on the frame number. The neural networks can be used for speech enhancement tasks such as dynamic noise suppression (DNS), blind source separation (BSS), and Self-Noise Silencers (SNS).

Abstract: Methods, apparatus, systems, and articles of manufacture to detect the location of sound sources external to computing devices are disclosed. An apparatus, to determine a direction of a source of a sound relative to a computing device, includes a cross-correlation analyzer to generate a vector of values corresponding to a cross-correlation of first and second audio signals corresponding to the sound. The first audio signal is received from a first microphone of the computing device. The second audio signal is received from a second microphone of the computing device. The apparatus also includes a location analyzer to use a machine learning model and a set of the values of the vector to determine the direction of the source of the sound.

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 and system of audio processing encodes cochlear-simulating spike data into spectrogram data.

Abstract: A method and system of neural network dynamic noise suppression is provided for audio processing.