DeLiang Wang – författare

How can we engineer systems capable of "cocktail party" listening? Human listeners are able to perceptually segregate one sound source from an acoustic mixture, such as a single voice from a mixture of other voices and music at a busy cocktail party. How can we engineer "machine listening" systems that achieve this perceptual feat?Albert Bregman's book Auditory Scene Analysis, published in 1990, drew an analogy between the perception of auditory scenes and visual scenes, and described a coherent framework for understanding the perceptual organization of sound. His account has stimulated much interest in computational studies of hearing. Such studies are motivated in part by the demand for practical sound separation systems, which have many applications including noise-robust automatic speech recognition, hearing prostheses, and automatic music transcription. This emerging field has become known as computational auditory scene analysis (CASA).Computational Auditory Scene Analysis: Principles, Algorithms, and Applications provides a comprehensive and coherent account of the state of the art in CASA, in terms of the underlying principles, the algorithms and system architectures that are employed, and the potential applications of this exciting new technology. With a Foreword by Bregman, its chapters are written by leading researchers and cover a wide range of topics including: Estimation of multiple fundamental frequenciesFeature-based and model-based approaches to CASASound separation based on spatial locationProcessing for reverberant environmentsSegregation of speech and musical signalsAutomatic speech recognition in noisy environmentsNeural and perceptual modeling of auditory organizationThe text is written at a level that will be accessible to graduate students and researchers from related science and engineering disciplines. The extensive bibliography accompanying each chapter will also make this book a valuable reference source. A web site accompanying the text, http://www.casabook.org, features software tools and sound demonstrations.

The three volume set LNCS 4232, LNCS 4233, and LNCS 4234 constitutes the refereed proceedings of the 13th International Conference on Neural Information Processing, ICONIP 2006, held in Hong Kong, China in October 2006. The 386 revised full papers presented were carefully reviewed and selected from 1175 submissions.

The three volume set LNCS 4232, LNCS 4233, and LNCS 4234 constitutes the refereed proceedings of the 13th International Conference on Neural Information Processing, ICONIP 2006, held in Hong Kong, China in October 2006. The 386 revised full papers presented were carefully reviewed and selected from 1175 submissions. The 126 papers of the first volume are organized in topical sections on neurobiological modeling and analysis, cognitive processing, mathematical modeling and analysis, learning algorithms, support vector machines, self-organizing maps, as well as independent component analysis and blind source separation. The second volume contains 128 contributions related to pattern classification, face analysis and processing, image processing, signal processing, computer vision, data pre-processing, forecasting and prediction, as well as neurodynamic and particle swarm optimization. The third volume offers 131 papers that deal with bioinformatics and biomedical applications, information security, data and text processing, financial applications, manufacturing systems, control and robotics, evolutionary algorithms and systems, fuzzy systems, and hardware implementations.

The three volume set LNCS 4232, LNCS 4233, and LNCS 4234 constitutes the refereed proceedings of the 13th International Conference on Neural Information Processing, ICONIP 2006, held in Hong Kong, China in October 2006. The 386 revised full papers presented were carefully reviewed and selected from 1175 submissions. The 126 papers of the first volume are organized in topical sections on neurobiological modeling and analysis, cognitive processing, mathematical modeling and analysis, learning algorithms, support vector machines, self-organizing maps, as well as independent component analysis and blind source separation. The second volume contains 128 contributions related to pattern classification, face analysis and processing, image processing, signal processing, computer vision, data pre-processing, forecasting and prediction, as well as neurodynamic and particle swarm optimization. The third volume offers 131 papers that deal with bioinformatics and biomedical applications, information security, data and text processing, financial applications, manufacturing systems, control and robotics, evolutionary algorithms and systems, fuzzy systems, and hardware implementations.