Monte Carlo smoothing with application to audio signal enhancement

信号处理 Pub Date : 2001-08-06 DOI:10.1109/SSP.2001.955211

W. Fong, S. Godsill, A. Doucet, M. West

引用次数: 153

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.

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蒙特卡罗平滑与应用于音频信号增强

我们描述了在非线性状态空间模型中应用蒙特卡罗滤波和平滑来估计未观测状态的方法。通过利用模型的统计结构，我们开发了一个Rao-Blackwellised粒子平滑器。用真实的语音和音频数据对该算法进行了测试，并将结果与使用通用粒子平滑器和扩展卡尔曼滤波产生的结果进行了比较。实验结果表明，该算法具有较好的效果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

信号处理

自引率

0.00%

发文量

5812

期刊介绍： Journal of Signal Processing is an academic journal supervised by China Association for Science and Technology and sponsored by China Institute of Electronics. The journal is an academic journal that reflects the latest research results and technological progress in the field of signal processing and related disciplines. It covers academic papers and review articles on new theories, new ideas, and new technologies in the field of signal processing. The journal aims to provide a platform for academic exchanges for scientific researchers and engineering and technical personnel engaged in basic research and applied research in signal processing, thereby promoting the development of information science and technology. At present, the journal has been included in the three major domestic core journal databases "China Science Citation Database (CSCD), China Science and Technology Core Journals (CSTPCD), Chinese Core Journals Overview" and Coaj. It is also included in many foreign databases such as Scopus, CSA, EBSCO host, INSPEC, JST, etc.