IEICE TRANSACTIONS on Information
Speech Enhancement Based on Noise Eigenspace Projection
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E92-D No.5 pp.1137-1145
- Publication Date
- 2009/05/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E92.D.1137
- Type of Manuscript
- PAPER
- Category
- Speech and Hearing
Authors
Keyword
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Dongwen YING, Masashi UNOKI, Xugang LU, Jianwu DANG, "Speech Enhancement Based on Noise Eigenspace Projection" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 5, pp. 1137-1145, May 2009, 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.1137/_p
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@ARTICLE{e92-d_5_1137,
author={Dongwen YING, Masashi UNOKI, Xugang LU, Jianwu DANG, },
journal={IEICE TRANSACTIONS on Information},
title={Speech Enhancement Based on Noise Eigenspace Projection},
year={2009},
volume={E92-D},
number={5},
pages={1137-1145},
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.},
keywords={},
doi={10.1587/transinf.E92.D.1137},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - Speech Enhancement Based on Noise Eigenspace Projection
T2 - IEICE TRANSACTIONS on Information
SP - 1137
EP - 1145
AU - Dongwen YING
AU - Masashi UNOKI
AU - Xugang LU
AU - Jianwu DANG
PY - 2009
DO - 10.1587/transinf.E92.D.1137
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2009
AB - 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.
ER -
English
