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The interest of AI in problems related to understanding sounds has a rich history dating back to the ARPA Speech Understanding Project in the 1970s. While a great deal has been learned from this and subsequent speech understanding research, the goal of building systems that can understand general acoustic signals--continuous speech and/or non-speech sounds--from unconstrained environments is still unrealized. Instead, there are now systems that understand "clean" speech well in relatively noiseless laboratory environments, but that break down in more realistic, noisier environments. As seen in the "cocktail-party effect," humans and other mammals have the ability to selectively attend to sound from a particular source, even when it is mixed with other sounds. Computers also need to be able to decide which parts of a mixed acoustic signal are relevant to a particular purpose--which part should be interpreted as speech, and which should be interpreted as a door closing, an air conditioner humming, or another person interrupting. Observations such as these have led a number of researchers to conclude that research on speech understanding and on nonspeech understanding need to be united within a more general framework. Researchers have also begun trying to understand computational auditory frameworks as parts of larger perception systems whose purpose is to give a computer integrated information about the real world. Inspiration for this work ranges from research on how different sensors can be integrated to models of how humans' auditory apparatus works in concert with vision, proprioception, etc. Representing some of the most advanced work on computers understanding speech, this collection of papers covers the work being done to integrate speech and nonspeech understanding in computer systems.
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.

IJCAI-97

International Joint Conferences on Artificial Intelligence

Published: 1997

Total Pages: 1720


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Auditory Scene Analysis addresses the problem of hearing complex auditory environments, using a series of creative analogies to describe the process required of the human auditory system as it analyzes mixtures of sounds to recover descriptions of individual sounds. In a unified and comprehensive way, Bregman establishes a theoretical framework that integrates his findings with an unusually wide range of previous research in psychoacoustics, speech perception, music theory and composition, and computer modeling.
This book is a printed edition of the Special Issue "Sound and Music Computing" that was published in Applied Sciences
This book presents a coherent state-of-the-art survey on the area of systematic and cognitive musicology which has enjoyed dynamic growth now for many years. It is devoted to exploring the relationships between acoustics, human information processing, and culture as well as to methodological issues raised by the widespread use of computers as a powerful tool for theory construction, theory testing, and the manipulation of musical information or any kind of data manipulation related to music.
This monograph provides novel insights into cognitive mechanisms underlying the processing of sound and music in different environments. A solid understanding of these mechanisms is vital for numerous technological applications such as for example information retrieval from distributed musical databases or building expert systems. In order to investigate the cognitive mechanisms of music perception fundamentals of hearing psychophysiology and principles of music perception are presented. In addition, some computational intelligence methods are reviewed, such as rough sets, fuzzy logic, artificial neural networks, decision trees and genetic algorithms. The applications of hybrid decision systems to problem solving in music and acoustics are exemplified and discussed on the basis of obtained experimental results.
The analysis of acousmatic music has traditionally been very difficult since there is no score to freeze the music in time. Analysis relies heavily on the act of concentrated listening. Since aural perception is so crucial to the analysis of acousmatic music, this book poses the questions: Can a framework for the analysis of acousmatic music be derived from cognition theories, research on the auditory perception of everyday environmental sounds, and studies into the perception of Western tonal music? If so, what are the frameworks attributes? From experimental data documented in the relevant literature, this book draws together the constituents of a cognitive framework called the Segregation, Integration, Assimilation and Meaning (SIAM) framework for the analysis of acousmatic music. The book reports on the practical application of the SIAM framework through a detailed analysis of the work Wind Chimes, by Denis Smalley. This analytical methodology should be especially useful to auditory cognition professionals, researchers interested in musical analysis of non-notated music, and composers seeking to gain more insight into musical structures in electroacoustic music in general.
The two-volume set LNCS 2686 and LNCS 2687 constitute the refereed proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2003, held in Maó, Menorca, Spain in June 2003. The 197 revised papers presented were carefully reviewed and selected for inclusion in the book and address the following topics: mathematical and computational methods in neural modelling, neurophysiological data analysis and modelling, structural and functional models of neurons, learning and other plasticity phenomena, complex systems dynamics, cognitive processes and artificial intelligence, methodologies for net design, bio-inspired systems and engineering, and applications in a broad variety of fields.
Comprehensive coverage of critical issues related to information science and technology.