Speech dereverberation is a signal processing technique of key importance for successful hands-free speech acquisition in applications of telecommunications and automatic speech recognition. Over the last few years, speech dereverberation has become a hot research topic driven by consumer demand, the availability of terminals based on Skype(TM) which encourage hands-free operation and the development of promising signal processing algorithms. Speech Dereverberation gathers together an overview, a mathematical formulation of the problem and the state-of-the-art solutions for dereverberation.

Speech Dereverberation presents the most important current approaches to the problem of reverberation. It begins by providing a focused and digestible review of the relevant topics in room acoustics and also describes key performance measures for dereverberation. The algorithms are then explained together with relevant mathematical analysis and supporting examples that enable the reader to see the relative strengths and weaknesses of the various techniques, as well as giving a clear understanding of the open questions still to be addressed in this topic. Techniques rooted in speech enhancement are included, in addition to a substantial treatment of multichannel blind acoustic system identification and inversion. The TRINICON framework is shown in the context of dereverberation to be a powerful generalization of the signal processing for a important range of analysis and enhancement techniques.

Speech Dereverberation offers the reader an overview of the subject area, as well as an in-depth text on the advanced signal processing involved. The book benefits the reader by providing such a wealth of information in one place, defines the current state of the art and, lastly, encourages further work on this topic by offering open research questions to exercise the curiosity of the reader. It is suitable for students at masters and doctorallevel, as well as established researchers.

Auteur

Patrick A. Naylor has a PhD in Speech Signal Processing from Imperial College London, where he is currently Reader and Director of Postgraduate Studies for the Department of Electrical and Electronic Engineering. His research interests include speech and audio signal processing; adaptive signal processing; speech enhancement in telecommunications; hands-free functionality; blind SIMO/MIMO channel estimation and dereverberation; speaker identification and verification; and speech production modelling. He is on the IEEE Technical Committee on Audio and Electroacoustics and is Associate Editor of the IEEE Transactions on Audio Speech and Language Processing.

Nikolay D. Gaubitch has a PhD in Acoustic Signal Processing from Imperial College London, where he is now Research Associate. In 2001 and 2002 he was awarded the Drapers' Company Undergraduate Prize for outstanding academic achievement. His research interests span various topics in single and multichannel speech and audio processing including dereverberation, blind system identification, acoustic system equalization and speech enhancement. He is a member of the IEEE.

Résumé

Speech Dereverberation gathers together an overview, a mathematical formulation of the problem and the state-of-the-art solutions for dereverberation.

Speech Dereverberation presents current approaches to the problem of reverberation. It provides a review of topics in room acoustics and also describes performance measures for dereverberation. The algorithms are then explained with mathematical analysis and examples that enable the reader to see the strengths and weaknesses of the various techniques, as well as giving an understanding of the questions still to be addressed. Techniques rooted in speech enhancement are included, in addition to a treatment of multichannel blind acoustic system identification and inversion. The TRINICON framework is shown in the context of dereverberation to be a generalization of the signal processing for a range of analysis and enhancement techniques.

Speech Dereverberation is suitable for students at masters and doctoral level, as well as established researchers.

Contenu
Models, Measurement and Evaluation.- Speech Dereverberation Using Statistical Reverberation Models.- Dereverberation Using LPC-based Approaches.- Multi-microphone Speech Dereverberation Using Eigen-decomposition.- Adaptive Blind Multichannel System Identification.- Subband Inversion of Multichannel Acoustic Systems.- Bayesian Single Channel Blind Dereverberation of Speech from a Moving Talker.- Inverse Filtering for Speech Dereverberation Without the Use of Room Acoustics Information.- TRINICON for Dereverberation of Speech and Audio Signals.