Abstract: This paper presents a smartphone app that performs real-time voice activity detection based on convolutional neural network. Real-time implementation issues are discussed showing how the slow inference time associated with convolutional neural networks is addressed. The developed smartphone app is meant to act as a switch for noise reduction in the signal processing pipelines of hearing devices, enabling noise estimation or classification to be conducted in noise-only parts of noisy speech signals. The developed smartphone app is compared with a previously developed voice activity detection app as well as with two highly cited voice activity detection algorithms. The experimental results indicate that the developed app using convolutional neural network outperforms the previously developed smartphone app.

You can find the paper for this GitHub repository : https://ieeexplore.ieee.org/document/8278160/

User's Guide

https://labs.utdallas.edu/ssprl/files/2020/09/16CNN-VAD-Video.mp4

The codes are licensed under MIT license.

For any utilization of the code content of this repository, one of the following books needs to get cited by the user:

A. Sehgal and N. Kehtarnavaz, "A Convolutional Neural Network Smartphone App for Real-Time Voice Activity Detection," IEEE Access, vol. 6, pp. 9017-9026, Feb 2018. (Open Access)

This work was supported in part by the National Institute of the Deafness and Other Communication Disorders (NIDCD) of the National

Institutes of Health (NIH) under Award 1R01DC015430. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH