A study on IMM with NPHMM and an application to speech enhancement
- Authors
- Lee, KY; Lee, J
- Issue Date
- Sep-2004
- Publisher
- ELSEVIER SCIENCE BV
- Keywords
- nonlinear speech enhancement; parallel-extended Kalman filter; nonlinear prediction HMM; neural network
- Citation
- SIGNAL PROCESSING, v.84, no.9, pp.1701 - 1707
- Journal Title
- SIGNAL PROCESSING
- Volume
- 84
- Number
- 9
- Start Page
- 1701
- End Page
- 1707
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/19968
- DOI
- 10.1016/j.sigpro.2004.05.015
- ISSN
- 0165-1684
- Abstract
- The nonlinear speech enhancement method with interactive parallel-extended Kalman filter is applied to speech contaminated by additive white noise. To represent the nonlinear and nonstationary nature of speech, we assume that speech is the output of a nonlinear prediction hidden Markov models (NPHMM) combining both neural network and HMM. The NPHMM is a nonlinear autoregressive process whose time-varying parameters are controlled by a hidden Markov chain. The simulation results shows that the proposed method offers better performance gains relative to the previous results (Signal Process 65 (1998) 373) with slightly increased complexity. (C) 2004 Elsevier B.V. All rights reserved.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Information Technology > ETC > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/19968)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.