Monte Carlo smoothing with application to audio signal enhancement

Author

Fong, William ; Godsill, Simon

Author_Institution

Signal Process. Group, Cambridge Univ., UK

fYear

2001

fDate

6/23/1905 12:00:00 AM

Firstpage

18

Lastpage

21

Abstract

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-Blackwellised particle smoother. The suggested algorithm is 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. It is found that the suggested algorithm gives better results

Keywords

Monte Carlo methods; audio signal processing; digital filters; nonlinear estimation; smoothing methods; speech enhancement; state-space methods; Monte Carlo filtering; Monte Carlo smoothing; Rao-Blackwellised particle smoother; audio signal enhancement; nonlinear state space model; speech data; statistical structure; unobserved states; Filtering; Hidden Markov models; Monte Carlo methods; Nonlinear filters; Particle filters; Signal processing; Signal processing algorithms; Smoothing methods; Speech; State-space methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal Processing, 2001. Proceedings of the 11th IEEE Signal Processing Workshop on

Print_ISBN

0-7803-7011-2

Type

conf

DOI

10.1109/SSP.2001.955211

Filename

955211