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このアイテムの引用には次の識別子を使用してください: http://hdl.handle.net/10119/9181

タイトル: Speech Enhancement based on Noise Eigenspace Projection
著者: Ying, Dongwen
Unoki, Masashi
Lu, Xugang
Dang, Jianwu
キーワード: speech enhancement
noise eigenspace
dimension reduction (DR)
Karhunen-Loeve transform (KLT)
発行日: 2009-05-01
出版者: 電子情報通信学会
誌名: IEICE Transactions on Information and Systems
巻: E92-D
号: 5
開始ページ: 1137
終了ページ: 1145
DOI: 10.1587/transinf.E92.D.1137
抄録: How to reduce noise with less speech distortion is a challenging issue for speech enhancement. We propose a novel approach for reducing noise with the cost of less speech distortion. A noise signal can generally be considered to consist of two components, a "white-like" component with a uniform energy distribution and a "color" component with a concentrated energy distribution in some frequency bands. An approach based on noise eigenspace projections is proposed to pack the color component into a subspace, named "noise subspace". This subspace is then removed from the eigenspace to reduce the color component. For the white-like component, a conventional enhancement algorithm is adopted as a complementary processor. We tested our algorithm on a speech enhancement task using speech data from the Texas Instruments and Massachusetts Institute of Technology (TIMIT) dataset and noise data from NOISEX-92. The experimental results show that the proposed algorithm efficiently reduces noise with little speech distortion. Objective and subjective evaluations confirmed that the proposed algorithm outperformed conventional enhancement algorithms.
Rights: Copyright (C)2009 IEICE. Dongwen Ying, Masashi Unoki, Xugang Lu, and Jianwu Dang, IEICE Transactions on Information and Systems, E92-D(5), 2009, 1137-1145. http://www.ieice.org/jpn/trans_online/
URI: http://hdl.handle.net/10119/9181
資料タイプ: publisher
出現コレクション:b10-1. 雑誌掲載論文 (Journal Articles)

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