Paper Abstract and Keywords |
Presentation |
2019-03-14 13:30
[Poster Presentation]
Diffuse noise reduction using adversarial denoising autoencoder Hikari Tanabe, Naohiro Tawara, Tetsunori Kobayashi (Waseda Univ.), Masaru Fujieda, Katagiri Kazuhiro, Takashi Yazu (OKI), Tetsuji Ogawa (Waseda Univ.) EA2018-125 SIP2018-131 SP2018-87 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In this study, we attempted to remove diffuse noise by a model combining a prefilter and an adversarial denoising autoencoder (aDAE). Since diffuse noise is caused by many noise sources or surface sound sources, it was difficult to remove diffuse noise by approaches assuming point sound sources. Deep learning based methods are expected to remove diffuse noise effectively since they are unnecessary to use assumption, but on the other hand, over fitting is likely to occur and the performance is deteriorated for unknown noise which is not used for training. In order to facilitate learning of aDAE, we used MVDR beamformer or ideal binary masking(idBM) as a pre-filter. We also introduced noise-aware training which feeds aDAE a noise-extracted signal as an auxiliary signal. Experiments using 2 channel microphones showed the effectiveness of pre-filter, aDAE with pre-filter outperformed original aDAE. Although MVDR beamformer cannot direct null angle to all interfering sources and its noise suppression is not strong, high performance is achieved by combining with aDAE. It was confirmed that noise-aware training is effective for aDAE with MVDR beamformer combined. By comparing pre-filters, it was revealed that MVDR beamformer is more suitable as a pre-filter of aDAE than idBM. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
noise reduction / adversarial generative network / MVDR beamformer / ideal-binary masking / noise-aware training / / / |
Reference Info. |
IEICE Tech. Rep., vol. 118, no. 497, SP2018-87, pp. 155-160, March 2019. |
Paper # |
SP2018-87 |
Date of Issue |
2019-03-07 (EA, SIP, SP) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
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EA2018-125 SIP2018-131 SP2018-87 |
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