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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/9181

Title: Speech Enhancement based on Noise Eigenspace Projection
Authors: Ying, Dongwen
Unoki, Masashi
Lu, Xugang
Dang, Jianwu
Keywords: speech enhancement
noise eigenspace
dimension reduction (DR)
Karhunen-Loeve transform (KLT)
Issue Date: 2009-05-01
Publisher: 電子情報通信学会
Magazine name: IEICE Transactions on Information and Systems
Volume: E92-D
Number: 5
Start page: 1137
End page: 1145
DOI: 10.1587/transinf.E92.D.1137
Abstract: 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
Material Type: publisher
Appears in Collections:b10-1. 雑誌掲載論文 (Journal Articles)

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