Speech enhancement by residual domain constrained optimization

Wen Jin, Michael S. Scordilis

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

A new algorithm for the enhancement of speech corrupted by additive noise is proposed. This algorithm estimates the linear prediction residuals of the clean speech using a constrained optimization criterion. The signal distortion is minimized in the residual domain subject to a constraint on the average power of the noise residuals. Enhanced speech is obtained by exciting the time-varying all-pole synthesis filter with the estimated residuals of the clean speech. The proposed method was tested with speech corrupted by both white Gaussian and colored noise. The enhancement performances were evaluated in terms of segmental signal-to-noise ratio (SNR) and ITU-PESQ scores. Experimental results indicate our method yields better enhancement results than a former residual-weighting scheme [Yegnanarayana, B., Avendano, C., Hermansky, H., Murthy P.S., 1999. Speech enhancement using linear prediction residual. Speech Commun. 28, 25-42]. The proposed method also achieves better noise reduction than the time-domain subspace method [Ephraim, Y., Van Trees, H.L., 1995. A signal subspace approach for speech enhancement. IEEE Trans. Speech Audio Process. 3, 251-266] on real world colored noise.

Original languageEnglish (US)
Pages (from-to)1349-1364
Number of pages16
JournalSpeech Communication
Volume48
Issue number10
DOIs
StatePublished - Oct 2006

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Keywords

  • Constrained optimization
  • Linear prediction
  • Speech enhancement

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Experimental and Cognitive Psychology
  • Linguistics and Language

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