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A soft decision-based speech enhancement using acoustic noise classification
- Choi, J.-H.;
- Kim, S.-K.;
- Chang, J.-H.
SCOPUS
0초록
In this letter, we present a speech enhancement technique based on the ambient noise classification incorporating the Gaussian mixture model (GMM). The principal parameters of the statistical model-based speech enhancement algorithm such as the weighting parameter in the decision-directed (DD) method and the long-term smoothing parameter of the noise estimation, are chosen as different values according to the classified contexts to ensure best performance for each noise. For the real-time environment awareness, the noise classification is performed on a frame-by-frame basis using the GMM with the soft decision framework. The speech absence probability (SAP) is used in detecting the speech absence periods and updating the likelihood of the GMM. Copyright ? 2011 ISCA.
키워드
- 제목
- A soft decision-based speech enhancement using acoustic noise classification
- 저자
- Choi, J.-H.; Kim, S.-K.; Chang, J.-H.
- 발행일
- 2011-08
- 유형
- Conference Paper
- 저널명
- Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
- 페이지
- 1193 ~ 1196