Proceedings of the Korean Information Science Society Conference (한국정보과학회:학술대회논문집)
- 2006.06b
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- Pages.151-153
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- 2006
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- 1598-5164(pISSN)
Rao-Blackwellized Particle Filtering for Sequential Speech Enhancement
Rao-Blackwellized particle filter를 이용한 순차적 음성 강조
- Park Sun-Ho (Postech, Computer Science and Engineering) ;
- Choi Seun-Jin (Postech, Computer Science and Engineering)
- Published : 2006.06.01
Abstract
we present a method of sequential speech enhancement, where we infer clean speech signal using a Rao-Blackwellized particle filter (RBPF), given a noise-contaminated observed signal. In contrast to Kalman filtering-based methods, we consider a non-Gaussian speech generative model that is based on the generalized auto-regressive (GAR) model. Model parameters are learned by a sequential Newton-Raphson expectation maximization (SNEM), incorporating the RBPF. Empirical comparison to Kalman filter, confirms the high performance of the proposed method.
Keywords