Exploring multi-channel features for denoising-autoencoder-based speech enhancement
Shoko Araki, Tomoki Hayashi, Marc Delcroix, Masakiyo Fujimoto, Kazuya Takeda, Tomohiro Nakatani
Abstract
This paper investigates a multi-channel denoising autoencoder (DAE)-based speech enhancement approach. In recent years, deep neural network (DNN)-based monaural speech enhancement and robust automatic speech recognition (ASR) approaches have attracted much attention due to their high performance. Although multi-channel speech enhancement usually outperforms single channel approaches, there has been little research on the use of multi-channel processing in the context of DAE. In this paper, we explore the use of several multi-channel features as DAE input to confirm whether multi-channel information can improve performance. Experimental results show that certain multi-channel features outperform both a monaural DAE and a conventional time-frequency-mask-based speech enhancement method.
BibTeX
@inproceedings{icassp2015_exploringmultich,
title = {Exploring multi-channel features for denoising-autoencoder-based speech enhancement},
author = {Shoko Araki and Tomoki Hayashi and Marc Delcroix and Masakiyo Fujimoto and Kazuya Takeda and Tomohiro Nakatani},
booktitle = {ICASSP 2015},
year = {2015}
}