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
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Zusammenfassung: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.
ISSN:1520-6149
DOI:10.1109/ICASSP.2015.7177943