Monte Carlo smoothing with application to audio signal enhancement

• Published in: IEEE transactions on signal processing Vol. 50; no. 2; pp. 438 - 449
• Main Authors: Fong, W., Godsill, S.J., Doucet, A., West, M.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2002; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Blocking; Computer simulation; Density functional theory; Filtering; Hidden Markov models; Mathematical models; Monte Carlo methods; Particle filters; Signal processing; Signal processing algorithms; Smoothing; Smoothing methods; State estimation; Storage capacity; Testing
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/78.978397

We describe methods for applying Monte Carlo filtering and smoothing for estimation of unobserved states in a nonlinear state-space model. By exploiting the statistical structure of the model, we develop a Rao-Blackwellized particle smoother. Due to the lengthy nature of real signals, we suggest processing the data in blocks, and a block-based smoother algorithm is developed for this purpose. All the algorithms suggested are tested with real speech and audio data, and the results are shown and compared with those generated using the generic particle smoother and the extended Kalman filter (EKF). It is found that the proposed Rao-Blackwellized particle smoother improves on the standard particle smoother and the extended Kalman smoother. In addition, the proposed block-based smoother algorithm enhances the efficiency of the proposed Rao-Blackwellized smoother by significantly reducing the storage capacity required for the particle information.