Exploring multi-channel features for denoising-autoencoder-based speech enhancement

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. Al...

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Veröffentlicht in:	2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) S. 116 - 120
Hauptverfasser:	Araki, Shoko, Hayashi, Tomoki, Delcroix, Marc, Fujimoto, Masakiyo, Takeda, Kazuya, Nakatani, Tomohiro
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.04.2015
Schlagworte:	Artificial neural networks Deep learning denoising autoencoder Filter banks multi-channel noise suppression Noise reduction PASCAL 'CHiME' challenge Testing Training
ISSN:	1520-6149
Online-Zugang:	Volltext
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