Abstract: A method comprises: storing n dimension (nD) dictionaries (nD dictionaries) where n decreases from a highest dimension to a lowest dimension, each nD dictionary including codewords for sequences of n symbols that are of a limited number that is less than all possible sequences of n symbols; storing key blocks for corresponding ones of the nD dictionaries, each key block configured with keys that map sequences of n?1 symbols to dictionaries of a corresponding one of the nD dictionaries that includes the codewords; receiving a sequence of symbols that represent indices of codevectors of a vector quantizer codebook that are representative of audio; determining a codeword using the key blocks and the nD dictionaries; and encoding a current symbol of the sequence of symbols using the codeword.

Abstract: A neural network audio codec system and related methods are provided. In one example, a method is provided comprising: obtaining speech audio to be encoded; applying the speech audio to an audio encoder that is part of a neural network audio codec system that includes the audio encoder and an audio decoder. The audio encoder and the audio decoder have been trained in an end-to-end manner. The speech audio is encoded with the audio encoder to generate embedding vectors that represent a snapshot of speech audio attributes over successive timeframes of the raw speech audio, and from the embedding vectors, codeword indices are generated to entries in a codebook. The codeword indices are then transmitted or stored for later retrieval and processing by the audio decoder.

Abstract: Presented herein are techniques in which a device detecting a phrase spoken in an online collaboration session between a plurality of users, the phrase being spoken by a first user to one or more second users. The device determines that the phrase indicates an issue with a quality of user experience of the online collaboration session, labels a log of metrics associated with the online collaboration session with a time stamp corresponding to a time when the phrase was spoken; and performs one or more actions to improve the user experience based on detecting the phrase.

Abstract: Presented herein are techniques in which a device detecting a phrase spoken in an online collaboration session between a plurality of users, the phrase being spoken by a first user to one or more second users. The device determines that the phrase indicates an issue with a quality of user experience of the online collaboration session, labels a log of metrics associated with the online collaboration session with a time stamp corresponding to a time when the phrase was spoken, to provide a labeled log of metrics; and performs one or more actions to improve the user experience based on detecting the phrase.

Abstract: Systems and methods are disclosed for audio enhancement. For example, methods may include accessing audio data; determining a window of audio samples based on the audio data; inputting the window of audio samples to a classifier to obtain a classification, in which the classifier includes a neural network and the classification takes a value from a set of multiple classes of audio; selecting, based on the classification, an audio enhancement network from a set of multiple audio enhancement networks; applying the selected audio enhancement network to the window of audio samples to obtain an enhanced audio segment, in which the selected audio enhancement network includes a neural network that has been trained using audio signals of a type associated with the classification; and storing, playing, or transmitting an enhanced audio signal based on the enhanced audio segment.

Abstract: Systems and methods are disclosed for audio enhancement. For example, methods may include accessing audio data; determining a window of audio samples based on the audio data; inputting the window of audio samples to a classifier to obtain a classification, in which the classifier includes a neural network and the classification takes a value from a set of multiple classes of audio; selecting, based on the classification, an audio enhancement network from a set of multiple audio enhancement networks; applying the selected audio enhancement network to the window of audio samples to obtain an enhanced audio segment, in which the selected audio enhancement network includes a neural network that has been trained using audio signals of a type associated with the classification; and storing, playing, or transmitting an enhanced audio signal based on the enhanced audio segment.

Abstract: Systems and methods are disclosed for audio group identification for conferencing. For example, methods may include joining a conference call using a network interface; accessing an audio signal that has been captured using a microphone; detecting a control signal in the audio signal; and, responsive to detection of the control signal, invoking modification of an audio path of the conference call.

Abstract: Systems and methods are disclosed for data driven radio enhancement. For example, methods may include demodulating a radio signal to obtain a demodulated audio signal; determining a window of audio samples based on the demodulated audio signal; applying an audio enhancement network to the window of audio samples to obtain an enhanced audio segment, in which the audio enhancement network includes a machine learning network that has been trained using demodulated audio signals derived from radio signals; and storing, playing, or transmitting an enhanced audio signal based on the enhanced audio segment.

Abstract: Systems and methods are disclosed for audio group identification for conferencing. For example, methods may include joining a conference call using a network interface; accessing an audio signal that has been captured using a microphone; detecting a control signal in the audio signal; and, responsive to detection of the control signal, invoking modification of an audio path of the conference call.

Abstract: Presented herein are techniques in which a device detecting a phrase spoken in an online collaboration session between a plurality of users, the phrase being spoken by a first user to one or more second users. The device determines that the phrase indicates an issue with a quality of user experience of the online collaboration session, labels a log of metrics associated with the online collaboration session with a time stamp corresponding to a time when the phrase was spoken, to provide a labeled log of metrics; and performs one or more actions to improve the user experience based on detecting the phrase.

Abstract: An endpoint selectively enhances a captured audio signal based on an operating mode. The endpoint obtains an audio input signal of multiple users in a physical location. The audio input signal is captured by a microphone. The endpoint separates voice signals from the audio input signal and determines an operating mode for an audio output signal. The endpoint selectively adjusts each of the voice signals based on the operating mode to generate the audio output signal.

Abstract: Systems and methods are disclosed for audio group identification for conferencing. For example, methods may include joining a conference call using a network interface; accessing an audio signal that has been captured using a microphone; detecting a control signal in the audio signal; and, responsive to detection of the control signal, invoking modification of an audio path of the conference call.

Abstract: Some embodiments perform, in a multi-layer neural network in a computing device, a convolution operation on input feature maps with multiple convolutional filters. The convolutional filters have multiple filter precisions. In other embodiments, electronic design automation (EDA) systems, methods, and computer-readable media are presented for adding such a multi-layer neural network into an integrated circuit (IC) design.

Abstract: Systems and methods are disclosed for audio enhancement. For example, methods may include accessing audio data; determining a window of audio samples based on the audio data; inputting the window of audio samples to a classifier to obtain a classification, in which the classifier includes a neural network and the classification takes a value from a set of multiple classes of audio; selecting, based on the classification, an audio enhancement network from a set of multiple audio enhancement networks; applying the selected audio enhancement network to the window of audio samples to obtain an enhanced audio segment, in which the selected audio enhancement network includes a neural network that has been trained using audio signals of a type associated with the classification; and storing, playing, or transmitting an enhanced audio signal based on the enhanced audio segment.

Abstract: Systems and methods are disclosed for data driven radio enhancement. For example, methods may include demodulating a radio signal to obtain a demodulated audio signal; determining a window of audio samples based on the demodulated audio signal; applying an audio enhancement network to the window of audio samples to obtain an enhanced audio segment, in which the audio enhancement network includes a machine learning network that has been trained using demodulated audio signals derived from radio signals; and storing, playing, or transmitting an enhanced audio signal based on the enhanced audio segment.

Abstract: Systems and methods are disclosed for audio group identification for conferencing. For example, methods may include joining a conference call using a network interface; accessing an audio signal that has been captured using a microphone; detecting a control signal in the audio signal; and, responsive to detection of the control signal, invoking modification of an audio path of the conference call.

Abstract: Some embodiments perform, in a multi-layer neural network in a computing device, optimization of the multi-layer neural network, for example by making a convolutional change with a first plurality of convolutional filters, or by making a connection change of a first plurality of convolutional filters. In other embodiments, electronic design automation (EDA) systems, methods, and computer-readable media are presented for adding such a multi-layer neural network into an integrated circuit (IC) design.

Abstract: Systems and methods are disclosed for audio enhancement. For example, methods may include accessing audio data; determining a window of audio samples based on the audio data; inputting the window of audio samples to a classifier to obtain a classification, in which the classifier includes a neural network and the classification takes a value from a set of multiple classes of audio; selecting, based on the classification, an audio enhancement network from a set of multiple audio enhancement networks; applying the selected audio enhancement network to the window of audio samples to obtain an enhanced audio segment, in which the selected audio enhancement network includes a neural network that has been trained using audio signals of a type associated with the classification; and storing, playing, or transmitting an enhanced audio signal based on the enhanced audio segment.

Abstract: Systems and methods are disclosed for data driven radio enhancement. For example, methods may include demodulating a radio signal to obtain a demodulated audio signal; determining a window of audio samples based on the demodulated audio signal; applying an audio enhancement network to the window of audio samples to obtain an enhanced audio segment, in which the audio enhancement network includes a machine learning network that has been trained using demodulated audio signals derived from radio signals; and storing, playing, or transmitting an enhanced audio signal based on the enhanced audio segment.

Abstract: Systems and methods are disclosed for data driven radio enhancement. For example, methods may include demodulating a radio signal to obtain a demodulated audio signal; determining a window of audio samples based on the demodulated audio signal; applying an audio enhancement network to the window of audio samples to obtain an enhanced audio segment, in which the audio enhancement network includes a machine learning network that has been trained using demodulated audio signals derived from radio signals; and storing, playing, or transmitting an enhanced audio signal based on the enhanced audio segment.