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- Yalta Nelson
- Intermedia Art and Science Department, Waseda University
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- Nakadai Kazuhiro
- Honda Research Institute Japan Co., Ltd.
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- Ogata Tetsuya
- Intermedia Art and Science Department, Waseda University
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抄録
<p>This study proposes the use of a deep neural network to localize a sound source using an array of microphones in a reverberant environment. During the last few years, applications based on deep neural networks have performed various tasks such as image classification or speech recognition to levels that exceed even human capabilities. In our study, we employ deep residual networks, which have recently shown remarkable performance in image classification tasks even when the training period is shorter than that of other models. Deep residual networks are used to process audio input similar to multiple signal classification (MUSIC) methods. We show that with end-to-end training and generic preprocessing, the performance of deep residual networks not only surpasses the block level accuracy of linear models on nearly clean environments but also shows robustness to challenging conditions by exploiting the time delay on power information.</p>
収録刊行物
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- Journal of Robotics and Mechatronics
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Journal of Robotics and Mechatronics 29 (1), 37-48, 2017-02-20
富士技術出版株式会社
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詳細情報 詳細情報について
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- CRID
- 1390564238047152640
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- NII論文ID
- 130007519882
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- NII書誌ID
- AA10809998
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- ISSN
- 18838049
- 09153942
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- NDL書誌ID
- 027998418
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可