Statistical models for speech dereverberation

Takuya Yoshioka, Hirokazu Kameoka, Tomohiro Nakatani, Hiroshi G. Okuno

研究成果: Conference contribution

2 引用 (Scopus)

抜粋

This paper discusses a statistical-model-based approach to speech dereverberation. With this approach, we first define parametric statistical models of probability density functions (pdfs) for a clean speech signal and a room transmission channel, then estimate the model parameters, and finally recover the clean speech signal by using the pdfs with the estimated parameter values. The key to the success of this approach lies in the definition of the models of the clean speech signal and room transmission channel pdfs. This paper presents several statistical models (including newly proposed ones) and compares them in a large-scale experiment. As regards the room transmission channel pdf, an autoregressive (AR) model, an autoregressive power spectral density (ARPSD) model, and a moving-average power spectral density (MAPSD) model are considered. A clean speech signal pdf model is selected according to the room transmission channel pdf model. The AR model exhibited the highest dereverberation accuracy when a reverberant speech signal of 2 sec or longer was available while the other two models outperformed the AR model when only a l-sec reverberant speech signal was available.

元の言語English
ホスト出版物のタイトルIEEE Workshop on Applications of Signal Processing to Audio and Acoustics
ページ145-148
ページ数4
DOI
出版物ステータスPublished - 2009
外部発表Yes
イベント2009 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2009 - New Paltz, NY
継続期間: 2009 10 182009 10 21

Other

Other2009 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2009
New Paltz, NY
期間09/10/1809/10/21

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications

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  • これを引用

    Yoshioka, T., Kameoka, H., Nakatani, T., & Okuno, H. G. (2009). Statistical models for speech dereverberation. : IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (pp. 145-148). [5346489] https://doi.org/10.1109/ASPAA.2009.5346489