Cisco Technology, Inc.

Dror Maydan

Samer Lutfi Hijazi

Christopher Rowen

Raul Alejandro Casas

Xuehong Mao

Hua Mu

Kamil Krzysztof Wojcicki

Marcin Ciolek

Erik Panu

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.

Data driven audio enhancement

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.

Audio group identification for conferencing

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Palo Alto, CA, United States of America

Dror Maydan