Single-channel speech enhancement: Using recurrent neuro-fuzzy voice activity detector and spectral subtraction algorithms

Fang Chen Chuang; Jeen Shing Wang; Li Ying Wu

doi:10.1109/ICSMC.2008.4811764

Single-channel speech enhancement: Using recurrent neuro-fuzzy voice activity detector and spectral subtraction algorithms

Fang Chen Chuang, Jeen Shing Wang, Li Ying Wu

Department of Electrical Engineering

Research output: Contribution to journal › Conference article › peer-review

Abstract

This paper investigates the effectiveness of a single-channel speech enhancement system that contains spectral subtraction and voice activity detection algorithm for noise elimination. We first extract features from a noisy signal and use these features as the inputs of a recurrent neuro-fuzzy network for detecting the voice activities of the signal. Based on the detection, we describe the characteristics of the background noise of the speech segments by a minimum frequency energy (MFE) parameter and then apply spectral subtraction algorithms with the parameter to eliminate the noise. Our simulation results show that the proposed enhancement system with a nonlinear spectral subtraction algorithm has superior performance.

Original language	English
Article number	4811764
Pages (from-to)	3057-3061
Number of pages	5
Journal	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
DOIs	https://doi.org/10.1109/ICSMC.2008.4811764
Publication status	Published - 2008
Event	2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008 - Singapore, Singapore Duration: 2008 Oct 12 → 2008 Oct 15

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering
Control and Systems Engineering
Human-Computer Interaction

Access to Document

10.1109/ICSMC.2008.4811764

Cite this

@article{59878e902e0c488f97f13af9abe41928,

title = "Single-channel speech enhancement: Using recurrent neuro-fuzzy voice activity detector and spectral subtraction algorithms",

abstract = "This paper investigates the effectiveness of a single-channel speech enhancement system that contains spectral subtraction and voice activity detection algorithm for noise elimination. We first extract features from a noisy signal and use these features as the inputs of a recurrent neuro-fuzzy network for detecting the voice activities of the signal. Based on the detection, we describe the characteristics of the background noise of the speech segments by a minimum frequency energy (MFE) parameter and then apply spectral subtraction algorithms with the parameter to eliminate the noise. Our simulation results show that the proposed enhancement system with a nonlinear spectral subtraction algorithm has superior performance.",

author = "Chuang, {Fang Chen} and Wang, {Jeen Shing} and Wu, {Li Ying}",

year = "2008",

doi = "10.1109/ICSMC.2008.4811764",

language = "English",

pages = "3057--3061",

journal = "Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics",

issn = "1062-922X",

note = "2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008 ; Conference date: 12-10-2008 Through 15-10-2008",

}

TY - JOUR

T1 - Single-channel speech enhancement

T2 - 2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008

AU - Chuang, Fang Chen

AU - Wang, Jeen Shing

AU - Wu, Li Ying

PY - 2008

Y1 - 2008

N2 - This paper investigates the effectiveness of a single-channel speech enhancement system that contains spectral subtraction and voice activity detection algorithm for noise elimination. We first extract features from a noisy signal and use these features as the inputs of a recurrent neuro-fuzzy network for detecting the voice activities of the signal. Based on the detection, we describe the characteristics of the background noise of the speech segments by a minimum frequency energy (MFE) parameter and then apply spectral subtraction algorithms with the parameter to eliminate the noise. Our simulation results show that the proposed enhancement system with a nonlinear spectral subtraction algorithm has superior performance.

AB - This paper investigates the effectiveness of a single-channel speech enhancement system that contains spectral subtraction and voice activity detection algorithm for noise elimination. We first extract features from a noisy signal and use these features as the inputs of a recurrent neuro-fuzzy network for detecting the voice activities of the signal. Based on the detection, we describe the characteristics of the background noise of the speech segments by a minimum frequency energy (MFE) parameter and then apply spectral subtraction algorithms with the parameter to eliminate the noise. Our simulation results show that the proposed enhancement system with a nonlinear spectral subtraction algorithm has superior performance.

UR - http://www.scopus.com/inward/record.url?scp=69949121377&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=69949121377&partnerID=8YFLogxK

U2 - 10.1109/ICSMC.2008.4811764

DO - 10.1109/ICSMC.2008.4811764

M3 - Conference article

AN - SCOPUS:69949121377

SN - 1062-922X

SP - 3057

EP - 3061

JO - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

JF - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

M1 - 4811764

Y2 - 12 October 2008 through 15 October 2008

ER -

Single-channel speech enhancement: Using recurrent neuro-fuzzy voice activity detector and spectral subtraction algorithms

Abstract

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this