Abstract: "Little research has been carried out in order to understand the mechanisms underlying the perception of polyphonic music. Perception of polyphonic music involves thematic recognition, that is, recognition of instances of theme through polyphonic voices, whether they appear unaccompanied, transposed, altered or not. There are many questions still open to debate concerning thematic recognition in the polyphonic domain. One of them, in particular, is the question of whether or not cognitive mechanisms of segmentation and thematic reinforcement facilitate thematic recognition in polyphonic music. This dissertation proposes a connectionist model to investigate the role of segmentation and thematic reinforcement in thematic recognition in polyphonic music. The model comprises two stages. The first stage consists of a supervised artificial neural model to segment musical pieces in accordance with three ases of rhythmic segmentation. The supervised model is trained and tested on sets of contrived patterns, and successfully applied to six musical pieces from J.S. Bach. The second stage consists of an original unsupervised artificial neural model to perform thematic recognition. The unsupervised model is trained and assessed on a four-part figure from J.S. Bach. The research carried out in this dissertation contributes into two distinct fields. Firstly, it contributes to the field of artificial neural networks. The original unsupervised model encodes and manipulates context information effectively, and that enables it to perform sequence classification and discrimination efficiently. It has application in cognitive domains which demand classifying either a set of sequences of vectors in time or sub-sequences within a unique and large sequence of vectors in time. Secondly, the research contributes to the field of music perception. The results obtained by the connectionist model suggest, along with other important conclusions, that thematic recognition in polyphony is not facilitated by segmentation but otherwise, facilitated by thematic reinforcement."

Annotation As one of our highest expressions of thought and creativity, music has always been a difficult realm to capture, model, and understand. The connectionist paradigm, now beginning to provide insights into many realms of human behavior, offers a new and unified viewpoint from which to investigate the subtleties of musical experience. Music and Connectionism provides a fresh approach to both fields, using the techniques of connectionism and parallel distributed processing to look at a wide range of topics in music research, from pitch perception to chord fingering to composition.The contributors, leading researchers in both music psychology and neural networks, address the challenges and opportunities of musical applications of network models. The result is a current and thorough survey of the field that advances understanding of musical phenomena encompassing perception, cognition, composition, and performance, and in methods for network design and analysis.Peter M. Todd is a doctoral candidate in the PDP Research Group of the Psychology Department at Stanford University. Gareth Loy is an award-winning composer, a lecturer in the Music Department of the University of California, San Diego, and a member of the technical staff of Frox Inc.Contributors. Jamshed J. Bharucha. Peter Desain. Mark Dolson. Robert Gjerclingen. Henkjan Honing. B. Keith Jenkins. Jacqueline Jons. Douglas H. Keefe. Tuevo Kohonen. Bernice Laden. Pauli Laine. Otto Laske. Marc Leman. J. P. Lewis. Christoph Lischka. D. Gareth Loy. Ben Miller. Michael Mozer. Samir I. Sayegh. Hajime Sano. Todd Soukup. Don Scarborough. Kalev Tiits. Peter M. Todd. Kari Torkkola.

This study is an historical and philosophical analysis of eight major theories that concern music perception, cognition, and meaning. These theories, developed in the 20th century, are among those most often cited by the music psychology and philosophy research literature. Included are Carl Seashore's theory of musical inheritance, Information theory, Mary Louise Serafine's theory of music as thought, music cognition versus speech cognition, neural network and Connectionist theory, and the musical meaning and communication theories of Susanne Langer, Leonard Meyer, and Peter Kivy.

This volume presents the most up-to-date collection of neural network models of music and creativity gathered together in one place. Chapters by leaders in the field cover new connectionist models of pitch perception, tonality, musical streaming, sequential and hierarchical melodic structure, composition, harmonization, rhythmic analysis, sound generation, and creative evolution. The collection combines journal papers on connectionist modeling, cognitive science, and music perception with new papers solicited for this volume. It also contains an extensive bibliography of related work. Contributors Shumeet Baluja, M.I. Bellgard, Michael A. Casey, Garrison W. Cottrell, Peter Desain, Robert O. Gjerdingen, Mike Greenhough, Niall Griffith, Stephen Grossberg, Henkjan Honing, Todd Jochem, Bruce F. Katz, John F. Kolen, Edward W. Large, Michael C. Mozer, Michael P.A. Page, Caroline Palmer, Jordan B. Pollack, Dean Pomerleau, Stephen W. Smoliar, Ian Taylor, Peter M. Todd, C.P. Tsang, Gregory M. Werner

For the past decade, Fiske (music, U. of Western Ontario) has been using neural network models to test his theory that musical thinking can be described as a hierarchy of progressively more intricate pattern-comparison activity, and that the resulting musical realizations are limited to only three cognitive category types. He describes the development of his theory, several related experimental studies, and the neural network models he uses to test the theory. Neural network methodology can seem daunting, he admits, so he has tried to keep technical descriptions to a minimum in order to highlight his main goal: to describe and test a set of principles that appear to represent the foundation of musical understanding. Annotation : 2004 Book News, Inc., Portland, OR (booknews.com).

This text comprises of reviews of work relating to music and mind. It presents a range of approaches from the psychological through the computational, to the musicological. The reviews were selected from papers submitted at the Third International Conference on Music Perception and Cognition Liege 1994 to illustrate the wide range of perspectives now being adopted in studying how humans make and respond to music. The book is divided ino five sections. The first part illustrates the role of analysis and ethnomusicology in understanding cultural determinants of musical behaviour. The second part charts what is known about aquisition of musical competence, from pre-birth through to the expert performer. The evidence accumulated about specific areas of the brain which control musical thinking and behaviour is examined in Part Three. The fourth part examines how neurological, behavioural and artificial intelligence approaches are converging to shed light on processes in auditory perception. Finally, Part Five highlights the important developments in how we conceptualize the way in which musical structures are represented in the mind.

Previously, artificial neural networks have been used to capture only the informal properties of music. However, cognitive scientist Michael Dawson found that by training artificial neural networks to make basic judgments concerning tonal music, such as identifying the tonic of a scale or the quality of a musical chord, the networks revealed formal musical properties that differ dramatically from those typically presented in music theory. For example, where Western music theory identifies twelve distinct notes or pitch-classes, trained artificial neural networks treat notes as if they belong to only three or four pitch-classes, a wildly different interpretation of the components of tonal music. Intended to introduce readers to the use of artificial neural networks in the study of music, this volume contains numerous case studies and research findings that address problems related to identifying scales, keys, classifying musical chords, and learning jazz chord progressions. A detailed analysis of the internal structure of trained networks could yield important contributions to the field of music cognition.

Familiarity underpins our engagement with music. This book highlights theoretical and empirical considerations about familiarity from three perspectives: listening, musicology and performance. Part I, ’Listening’, addresses familiarity as it relates to listeners’ behaviour and responses to music, specifically in regulating our choice and exposure to music on a daily basis; how we get to know music through regular listening; how comfortable we feel in a Western concert environment; and music’s efficacy as a pain-reliever. Part II, ’Musicology’ exposes the notion of familiarity from varied stances, including appreciation of music in our own and other cultures through ethnomusicology; exploration of the perception of sounds via music analysis; philosophical reflection on the efficiency of communication in musicology; evaluation of the impact of researchers’ musical experiences on their work; and the influence of familiarity in music education. Part III, ’Performance’, focuses on the effects of familiarity in relation to different aspects of Western art and popular performance, including learning and memorizing music; examination of ’groove’ in popular performance; exploration of the role of familiarity in shaping socio-emotional behaviour between members of an ensemble; and consideration about the effects of the unique type of familiarity gained by musicians through the act of performance itself.

A comprehensive, up-to-date introduction to the psychology of musical development in children and adults, from theory to research and applications.