Machine Audition: Principles, Algorithms and Systems

Machine Audition: Principles, Algorithms and Systems

Wenwu Wang (University of Surrey, UK)
Indexed In: SCOPUS View 1 More Indices
Release Date: July, 2010|Copyright: © 2011 |Pages: 554
DOI: 10.4018/978-1-61520-919-4
ISBN13: 9781615209194|ISBN10: 1615209190|EISBN13: 9781615209200
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Description & Coverage
Description:

Machine audition is the study of algorithms and systems for the automatic analysis and understanding of sound by machine. It has recently attracted increasing interest within several research communities, such as signal processing, machine learning, auditory modeling, perception and cognition, psychology, pattern recognition, and artificial intelligence. However, the developments made so far are fragmented within these disciplines, lacking connections and incurring potentially overlapping research activities in this subject area.

Machine Audition: Principles, Algorithms and Systems contains advances in algorithmic developments, theoretical frameworks, and experimental research findings. This book is useful for professionals who want an improved understanding about how to design algorithms for performing automatic analysis of audio signals, construct a computing system for understanding sound, and learn how to build advanced human-computer interactive systems.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • Acoustic transmission channels
  • Alaryngeal speech signals
  • Audio source separation
  • Automatic sound source localization
  • Functional requirements of auditory systems
  • Machine-based multi-channel source separation
  • Multimodal emotion recognition
  • Music information retrieval
  • Nonnegative matrix factorization
  • Pattern recognition techniques
Reviews and Testimonials

"The book is the first of its kind that describes the theoretical, algorithmic and systematic results from the area of machine audition. It intends to promote "machine audition" as a subject area that is equally attractive to the popular subject of "computer vision". The book treats audition in the context of general audio, rather than for specific data, such as speech in some existing literature. It contains many new approaches and algorithms, most recent numerical and experimental results, which could foster a better understanding of the state of the art of the subject and ultimately motivate novel ideas and thinking in the research communities. A unique characteristic about the book is that it brings together the fragments of the research findings in machine audition research across several disciplines, which could potentially promote cutting-edge research in this subject area."

– Wenwu Wang, University of Surrey
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Editor/Author Biographies
Wenwu Wang is a Lecturer at Centre for Vision Speech and Signal Processing, University of Surrey, where he joined since May 2007. Prior to this, he was a Postdoctoral Research Associate at King's College London (from May 2002 to December 2003) and Cardiff University (from January 2004 to April 2005). He also worked in UK industry, first as a DSP Engineer at Tao Group Ltd (now Antix Labs Ltd) (from May 2005 to August 2006), then as an R&D engineer at Creative Labs (from September 2006 to April 2007). During spring 2008, he has been a visiting scholar at the Perception and Neurodynamics Lab and the Center for Cognitive Science, The Ohio State University. He is part of the MOD University Defense Research Centre in Signal Processing. He obtained the PhD degree in April 2002 from Harbin Engineering University, China. His research interests include blind signal processing, audio-visual signal processing, machine learning and perception, and machine audition (listening). He is a member of the IEEE, and belongs to the IEEE Signal Processing, and Circuits and Systems Societies.
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Editorial Review Board
  • Jonathon Chambers, Loughborough University, UK
  • Jingdong Chen, Bell Labs, USA
  • Shlomo Dubnov, University of California at San Diego, USA
  • Philip Jackson, University of Surrey, UK
  • Maria Jafari, Queen Mary University of London, UK
  • Gerasimos Potamianos, Foundation for Research and Technology, Greece
  • Gaël Richard, TELECOM ParisTech, France
  • Saeid Sanei, Cardiff University, UK
  • Paris Smaragdis, Adobe Systems, USA
  • George Tzanetakis, University of Victoria, Canada