EMD-based noise estimation and tracking (ENET) with application to speech enhancement

N. Chatlani, J.J. Soraghan

Research output: Contribution to conferencePaper

7 Citations (Scopus)

Abstract

Speech enhancement from measured speech signals is fundamental in a wide range of instruments. It relies on a noise estimate which can be obtained using techniques such as the minimum statistics (MS) approach. In this paper, a novel
approach for Empirical Mode Decomposition (EMD) based noise estimation and tracking (EET) is presented with application to speech enhancement. Spectral analysis of nonstationary signals such as speech is performed effectively
using EMD. The Improved Minima Controlled Recursive Averaging (IMCRA) that evolved from MS has been shown to be effective in non-stationary environments. EET is able to use EMD in a novel way to estimate the noise spectrum more accurately than IMCRA and enhance speech more effectively than conventional log-MMSE approaches. A comparative performance study is included that demonstrates that it achieves improved speech quality than a conventional
log-MMSE filtering approach with better noise estimation, even during periods of strong speech activity.
LanguageEnglish
Pages180-184
Number of pages4
Publication statusPublished - Aug 2009
Event17th European Signal Processing Conference - Glasgow, Scotland
Duration: 24 Aug 200928 Aug 2009

Conference

Conference17th European Signal Processing Conference
CityGlasgow, Scotland
Period24/08/0928/08/09

Fingerprint

Speech enhancement
Decomposition
Statistics
Spectrum analysis

Keywords

  • ENET Analysis
  • EMD-based noise estimation
  • speech enhancement
  • empirical mode decomposition
  • improved minima controlled recursive averaging

Cite this

Chatlani, N., & Soraghan, J. J. (2009). EMD-based noise estimation and tracking (ENET) with application to speech enhancement. 180-184. Paper presented at 17th European Signal Processing Conference, Glasgow, Scotland, .
Chatlani, N. ; Soraghan, J.J. / EMD-based noise estimation and tracking (ENET) with application to speech enhancement. Paper presented at 17th European Signal Processing Conference, Glasgow, Scotland, .4 p.
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Chatlani, N & Soraghan, JJ 2009, 'EMD-based noise estimation and tracking (ENET) with application to speech enhancement' Paper presented at 17th European Signal Processing Conference, Glasgow, Scotland, 24/08/09 - 28/08/09, pp. 180-184.

EMD-based noise estimation and tracking (ENET) with application to speech enhancement. / Chatlani, N.; Soraghan, J.J.

2009. 180-184 Paper presented at 17th European Signal Processing Conference, Glasgow, Scotland, .

Research output: Contribution to conferencePaper

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AU - Soraghan, J.J.

PY - 2009/8

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N2 - Speech enhancement from measured speech signals is fundamental in a wide range of instruments. It relies on a noise estimate which can be obtained using techniques such as the minimum statistics (MS) approach. In this paper, a novelapproach for Empirical Mode Decomposition (EMD) based noise estimation and tracking (EET) is presented with application to speech enhancement. Spectral analysis of nonstationary signals such as speech is performed effectivelyusing EMD. The Improved Minima Controlled Recursive Averaging (IMCRA) that evolved from MS has been shown to be effective in non-stationary environments. EET is able to use EMD in a novel way to estimate the noise spectrum more accurately than IMCRA and enhance speech more effectively than conventional log-MMSE approaches. A comparative performance study is included that demonstrates that it achieves improved speech quality than a conventionallog-MMSE filtering approach with better noise estimation, even during periods of strong speech activity.

AB - Speech enhancement from measured speech signals is fundamental in a wide range of instruments. It relies on a noise estimate which can be obtained using techniques such as the minimum statistics (MS) approach. In this paper, a novelapproach for Empirical Mode Decomposition (EMD) based noise estimation and tracking (EET) is presented with application to speech enhancement. Spectral analysis of nonstationary signals such as speech is performed effectivelyusing EMD. The Improved Minima Controlled Recursive Averaging (IMCRA) that evolved from MS has been shown to be effective in non-stationary environments. EET is able to use EMD in a novel way to estimate the noise spectrum more accurately than IMCRA and enhance speech more effectively than conventional log-MMSE approaches. A comparative performance study is included that demonstrates that it achieves improved speech quality than a conventionallog-MMSE filtering approach with better noise estimation, even during periods of strong speech activity.

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KW - EMD-based noise estimation

KW - speech enhancement

KW - empirical mode decomposition

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Chatlani N, Soraghan JJ. EMD-based noise estimation and tracking (ENET) with application to speech enhancement. 2009. Paper presented at 17th European Signal Processing Conference, Glasgow, Scotland, .