A robust estimation method of noise mixture model for noise suppression

Masakiyo Fujimoto, Shinji Watanabe, Tomohiro Nakatani

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Vector Taylor series (VTS)-based noise suppression usually employs a single Gaussian distribution for the noise model. However, it is insufficient for non-stationary noise which has a multi-peak distribution. It is very complex to estimate multi-peak distribution of the noise, when we deal with the noise as random variables or hidden variables. To solve these problems, we investigate a way of estimating the noise mixture model by using a minimum mean squared error (MMSE) estimate of the noise. By iterating the MMSE estimation of noise and noise model estimation, the proposed method realizes the simultaneous optimization of both the observed signal model and the noise model. The proposed method significantly outperformed the VTS-based approach, and the maximum improvement in the word error rate was about 12%.

Original languageEnglish
Pages (from-to)697-700
Number of pages4
JournalUnknown Journal
Publication statusPublished - 2011
Externally publishedYes

    Fingerprint

Keywords

  • MMSE estimation
  • Noise model estimation
  • Noise suppression

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modelling and Simulation

Cite this