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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| Vydáno v: | 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) s. 116 - 120 |
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| Jazyk: | angličtina |
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IEEE
01.04.2015
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| ISSN: | 1520-6149 |
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| 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. |
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| AbstractList | 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. |
| Author | Araki, Shoko Hayashi, Tomoki Fujimoto, Masakiyo Takeda, Kazuya Nakatani, Tomohiro Delcroix, Marc |
| Author_xml | – sequence: 1 givenname: Shoko surname: Araki fullname: Araki, Shoko organization: NTT Commun. Sci. Labs., NTT Corp., Kyoto, Japan – sequence: 2 givenname: Tomoki surname: Hayashi fullname: Hayashi, Tomoki organization: NTT Commun. Sci. Labs., NTT Corp., Kyoto, Japan – sequence: 3 givenname: Marc surname: Delcroix fullname: Delcroix, Marc organization: NTT Commun. Sci. Labs., NTT Corp., Kyoto, Japan – sequence: 4 givenname: Masakiyo surname: Fujimoto fullname: Fujimoto, Masakiyo organization: NTT Commun. Sci. Labs., NTT Corp., Kyoto, Japan – sequence: 5 givenname: Kazuya surname: Takeda fullname: Takeda, Kazuya organization: Dept. of Media Sci., Nagoya Univ., Nagoya, Japan – sequence: 6 givenname: Tomohiro surname: Nakatani fullname: Nakatani, Tomohiro organization: NTT Commun. Sci. Labs., NTT Corp., Kyoto, Japan |
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| Snippet | This paper investigates a multi-channel denoising autoencoder (DAE)-based speech enhancement approach. In recent years, deep neural network (DNN)-based... |
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| SubjectTerms | Artificial neural networks Deep learning denoising autoencoder Filter banks multi-channel noise suppression Noise reduction PASCAL 'CHiME' challenge Testing Training |
| Title | Exploring multi-channel features for denoising-autoencoder-based speech enhancement |
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