With human-computer interactions and hands-free communications becoming overwhelmingly important in the new millennium, recent research efforts have been increasingly focusing on state-of-the-art multi-microphone signal processing solutions to improve speech intelligibility in adverse environments. One such prominent statistical signal processing technique is blind signal separation (BSS). BSS was first introduced in the early 1990s and quickly emerged as an area of intense research activity showing huge potential in numerous applications. BSS comprises the task of 'blindly' recovering a set of unknown signals, the so-called sources from their observed mixtures, based on very little to almost no prior knowledge about the source characteristics or the mixing structure. The goal of BSS is to process multi-sensory observations of an inaccessible set of signals in a manner that reveals their individual (and original) form, by exploiting the spatial and temporal diversity, readily accessible through a multi-microphone configuration. Proceeding blindly exhibits a number of advantages, since assumptions about the room configuration and the source-to-sensor geometry can be relaxed without affecting overall efficiency. This booklet investigates one of the most commercially attractive applications of BSS, which is the simultaneous recovery of signals inside a reverberant (naturally echoing) environment, using two (or more) microphones. In this paradigm, each microphone captures not only the direct contributions from each source, but also several reflected copies of the original signals at different propagation delays. These recordings are referred to as the convolutive mixtures of the original sources. The goal of this booklet in the lecture series is to provide insight on recent advances in algorithms, which are ideally suited for blind signal separation of convolutive speech mixtures. More importantly, specific emphasis is given in practical applications of the developed BSS algorithms associated with real-life scenarios. The developed algorithms are put in the context of modern DSP devices, such as hearing aids and cochlear implants, where design requirements dictate low power consumption and call for portability and compact size. Along these lines, this booklet focuses on modern BSS algorithms which address (1) the limited amount of processing power and (2) the small number of microphones available to the end-user. Table of Contents: Fundamentals of blind signal separation / Modern blind signal separation algorithms / Application of blind signal processing strategies to noise reduction for the hearing-impaired / Conclusions and future challenges / Bibliography

Autorentext

Kostas Kokkinakis graduated from the University of Sheffield, United Kingdom, with the B.S. degree in Electronics, Control and Systems Engineering in 2000. He then received the M.S. degree in Microelectronics and Signal Processing from the University of London and the Ph.D. degree in Electronics from the University of Liverpool, in 2001 and 2005, respectively. Dr. Kokkinakis is currently a Research Assistant Professor in the Erik Jonsson School of Engineering and Computer Science at the University of Texas at Dallas, Richardson, TX, working on multi-microphone signal processing strategies for speech enhancement. The focus of his research work is on statistical signal processing, psychoacoustics, speech modeling and noise reduction. His research interests lie mainly in the development of blind signal separation strategies and their application to cochlear implant devices. His research is supported by funding from the National Institute on Deaf[1]ness and other Communication Disorders of the National Institutes of Health. Philipos C. Loizou received the B.S., M.S., and Ph.D. degrees, all in Electrical Engineering, from Arizona State University (ASU), Tempe, AZ, in 1989, 1991, and 1995, respectively. From 1995 to 1996, he was a Postdoctoral Fellow in the Department of Speech and Hearing Science at ASU, working on research related to cochlear implants. He was an Assistant Professor at the University of Arkansas at Little Rock from 1996 to 1999. He is now a Professor and holder of the Cecil and Ida Green Chair in the Department of Electrical Engineering, University of Texas at Dallas, Richardson, TX. His research interests are in the areas of signal processing, speech processing and cochlear implants. Dr. Loizou is currently working on the development of novel speech processing algorithms that will aid people with hearing impairment, and in particularly, people wearing cochlear implants. He is author of the book Speech Enhancement: Theory and Practice (CRC, 2007) and co-author of the textbook An Interactive Approach to Signals and Systems Laboratory (National Instruments, 2008). Dr. Loizou is a Fellow of the Acoustical Society of America and a member of the Speech Technical Committee of the IEEE Signal Processing Society. His research is supported by funding from the National Institute on Deafness and other Communication Disorders of the National Institutes of Health.

Inhalt
Fundamentals of blind signal separation.- Modern blind signal separation algorithms.- Application of blind signal processing strategies to noise reduction for the hearing-impaired.- Conclusions and future challenges.- Bibliography.