Speech Enhancement in Multiple-Noise Conditions Using Deep Neural Networks

Anurag Kumar, Dinei Florencio


In this paper we consider the problem of speech enhancement in real-world like conditions where multiple noises can simultaneously corrupt speech. Most of the current literature on speech enhancement focus primarily on presence of single noise in corrupted speech which is far from real-world environments. Specifically, we deal with improving speech quality in office environment where multiple stationary as well as non-stationary noises can be simultaneously present in speech. We propose several strategies based on Deep Neural Networks (DNN) for speech enhancement in these scenarios. We also investigate a DNN training strategy based on psychoacoustic models from speech coding for enhancement of noisy speech.


DOI: 10.21437/Interspeech.2016-88

Cite as

Kumar, A., Florencio, D. (2016) Speech Enhancement in Multiple-Noise Conditions Using Deep Neural Networks. Proc. Interspeech 2016, 3738-3742.

Bibtex
@inproceedings{Kumar+2016,
author={Anurag Kumar and Dinei Florencio},
title={Speech Enhancement in Multiple-Noise Conditions Using Deep Neural Networks},
year=2016,
booktitle={Interspeech 2016},
doi={10.21437/Interspeech.2016-88},
url={http://dx.doi.org/10.21437/Interspeech.2016-88},
pages={3738--3742}
}