leoray1990 / cnn-vad

A Convolutional Neural Network based Voice Activity Detector for Smartphones

Convolutional Neural Network based Voice Activity Detector

This GitHub repository is the code accompaniment of the following paper:

A Convolutional Neural Network Smartphone App for Real-Time Voice Activity Detection
Abhishek Sehgal and Nasser Kehtarnavaz - University of Texas at Dallas
https://ieeexplore.ieee.org/document/8278160/

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.

Resources

Supporting materials related to thsi work are available via the following links:

Link	Description
https://ieeexplore.ieee.org/document/8278160/	IEEE Access Manuscript
http://www.utdallas.edu/~kehtar/CNN-VAD.mp4	Videoclip of Convolutional Neural Network VAD running in real-time on Android and iOS smartphone platforms

Getting Started

A User's Guide is provided which describes how to run the codes for training and real-time operation on Android and iOS smartphones platforms.

License and Citation

The codes are licensed under MIT license.

For any utilization of the code content of this repository, the following paper 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)