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Model-based Analysis-by-Synthesis for the Dereverberation of Speech and Audio Signals

基于模型的语音和音频信号去混响分析

基本信息

批准号：
62235747
负责人：
Professor Dr.-Ing. Gerald Enzner
金额：
--
依托单位：
Institut für Kommunikationsakustik
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2008
资助国家：
德国
起止时间：
2007-12-31 至 2013-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/62235747?language=en
关键词：
Model based Analysis Synthesis Dereverberation

项目摘要

This project deals with the dereverberation of speech signals by means of multichannel blind channel identification and equalization. This final proposal aims at a systematic enhancement of the current approach by using statistical methods. The upcoming optimization will be based on Bayesian inference principles that extend the existing Maximum Likelihood (ML) solution and a Maximum A Posterior (MAP) estimator towards a Variational Bayes (VB) estimation algorithm.Strong models for the unknown acoustic system and the source signal allow for the derivation of powerful VB algorithms. Based upon the existing Markov model for the channels, this last project phase will particularly address the modeling of the source signal. By introducing a priori information about the unknown quantity, the resulting algorithms will then be specifically tailored to the dereverberation problem in order to mitigate remaining estimation errors. The optimization in a VB sense should eventually lead to an efficient estimation algorithm that adaptively estimates the channel and the source signal as well as the corresponding model parameters. Such a systematic enhancement of the proposed system from ML to MAP and VB moreover allows for a structural comparison of all three estimation approaches and an analysis of their individual strengths and shortcomings. All this will help to assess the overall benefit of the proposed methods.

本课题采用多通道盲信道辨识与均衡的方法对语音信号进行去混响处理。这一最后建议旨在通过使用统计方法系统地加强目前的方法。即将到来的优化将基于贝叶斯推理原理，将现有的最大似然（ML）解决方案和最大后验（MAP）估计器扩展到变分贝叶斯（VB）估计算法。未知声学系统和源信号的强模型允许推导强大的VB算法。基于现有的信道马尔可夫模型，这最后一个项目阶段将特别解决源信号的建模问题。通过引入关于未知量的先验信息，所得到的算法然后将被专门定制为去混响问题，以减轻剩余的估计误差。VB意义上的优化最终将导致自适应地估计信道和源信号以及相应的模型参数的有效估计算法。此外，这种系统性的增强所提出的系统从ML到MAP和VB允许对所有三种估计方法进行结构比较，并分析它们各自的优点和缺点。所有这些都将有助于评估拟议方法的总体效益。