A soft decision-based speech enhancement using acoustic noise classification
- Title
- A soft decision-based speech enhancement using acoustic noise classification
- Author
- 장준혁
- Keywords
- Gaussian mixture model; Noise classification; Soft decision; Speech enhancement
- Issue Date
- 2011-08
- Publisher
- Proceedings of the Annual Conference of the International Speech Communication Association
- Citation
- INTERSPEECH, 2011, P.1193-1196
- Abstract
- 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.
- URI
- https://www.isca-speech.org/archive/interspeech_2011/i11_1193.htmlhttps://repository.hanyang.ac.kr/handle/20.500.11754/72886
- ISSN
- 1990-9772
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > ELECTRONIC ENGINEERING(융합전자공학부) > Articles
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