DOCSLIB.ORG

Modeling Musical Anticipation: from the Time of Music to the Music of Time

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Modeling Musical Anticipation: from the Time of Music to the Music of Time UC San Diego UC San Diego Electronic Theses and Dissertations Title Modeling musical anticipation : from the time of music to the music of time Permalink https://escholarship.org/uc/item/4nv162ms Author Cont, Arshia Publication Date 2008 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA, SAN DIEGO Modeling Musical Anticipation: From the time of music to the music of time A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Music by Arshia Cont Committee in charge: Shlomo Dubnov, Chair Alain de Cheveigné Philippe Manoury Miller Puckette Lawrence Saul David Wessel 2008 Copyright Arshia Cont, 2008 All rights reserved. The dissertation of Arshia Cont is approved, and it is acceptable in quality and form for publication on microfilm and electronically: Chair University of California, San Diego 2008 iii DEDICATION !"#$%&'!()*+,-.&'!() /0123456!78. 9: CD JKL $ 3;<=>?@6AB 5EFGHI MN Dedicated to ones who dedicate... my parents Mansoureh Daneshkazemi and Gholamhossen Cont iv ! EPIGRAPH “I think that the search for a universal answer to the questions raised by musical experi- ence will never be completely fulfilled; but we know that a question raised is often more significant than the answer received. Only a reckless spirit, today, would try to give a total explanation of music, but anyone who would never pose the problem is even more reckless.” Remembering the future LUCIANO BERIO v TABLE OF CONTENTS Signature Page............................................ iii Dedication.............................................. iv Epigraph...............................................v Table of Contents.......................................... vi List of Figures............................................ xi List of Tables............................................ xiii Acknowledgements........................................ xiv Vita................................................... xvii Abstract................................................ xviii Chapter 1. Introduction......................................1 1.1. Approach........................................3 1.2. Organization......................................5 1.3. Contributions......................................7 I From Modeling Anticipation to Anticipatory Modeling9 Chapter 2. Modeling Musical Anticipation......................... 10 2.1. Psychology of musical expectation........................ 11 2.1.1. Experimental Research Scopes..................... 11 2.1.2. Auditory Learning.............................. 13 2.1.3. Concurrent and Competitive Representations............ 14 2.1.4. Mental Representations of Expectation................ 15 2.2. Anticipation Defined................................. 17 2.2.1. Anticipation in view of Expectation.................. 17 2.2.2. Anticipation in view of Enaction.................... 18 2.2.3. Anticipation in view of Computation.................. 19 2.3. Models of Musical Expectation.......................... 20 2.3.1. Music Theoretic Models.......................... 21 2.3.2. Automatic Learning Models....................... 23 2.3.3. Information Theoretic Models...................... 25 2.4. Modeling Investigations............................... 29 2.4.1. Imperfect Heuristics and Naive Realism............... 30 vi 2.4.2. Over-intellectualization of the intellect................ 33 2.4.3. Scientific pluralism............................. 34 2.5. Summary........................................ 35 Chapter 3. Anticipatory Modeling............................... 38 3.1. Anticipatory Computing............................... 39 3.2. General Modeling Framework........................... 41 3.2.1. Markov Decision Process Framework................. 42 3.2.2. Interactive Learning in an Environment................ 44 3.3. Distinctions of Anticipatory Behavior...................... 45 3.3.1. Implicit Anticipation............................ 46 3.3.2. Payoff Anticipation............................. 47 3.3.3. Sensorial Anticipation........................... 47 3.3.4. State Anticipation.............................. 48 3.4. Learning Approaches................................. 49 3.4.1. Reinforcement Learning.......................... 50 3.4.2. Learning Classifier Systems....................... 51 3.5. Modeling Implications................................ 52 3.5.1. Information as Available.......................... 52 3.5.2. Interactive and on-line Learning..................... 53 3.5.3. Multimodal Interaction and Modeling................. 54 II What to Expect 56 Chapter 4. Music Information Geometry........................... 57 4.1. General Discussions................................. 57 4.2. Preliminaries...................................... 60 4.2.1. Information Geometry of Statistical Structures........... 61 4.2.2. Elements of Bregman Geometry..................... 63 4.2.3. Exponential Family of Distributions.................. 68 4.2.4. Bregman Geometry and Exponential distributions......... 70 4.3. Music Information Geometry........................... 74 4.3.1. Methodology................................. 74 4.3.2. Data IR..................................... 76 4.3.3. Model IR.................................... 77 4.4. From Divergence to Similarity Metric...................... 79 4.4.1. Symmetrized Bregman Divergences.................. 81 4.4.2. Triangle Inequality............................. 82 4.5. Incremental Model Formations.......................... 83 4.6. Discussions....................................... 87 vii Chapter 5. Methods of Information Access......................... 89 5.1. Incremental Clustering and Structure Discovery............... 89 5.1.1. Related Works................................ 90 5.1.2. Audio Oracle Data Structure....................... 93 5.1.3. Audio Oracle Learning and Construction............... 96 5.1.4. Sample Results................................ 99 5.1.5. Discussions.................................. 102 5.2. Guidage: Fast Query-Based Information Retrieval.............. 104 5.2.1. Research Scope............................... 105 5.2.2. Related Works................................ 107 5.2.3. General Framework............................. 109 5.2.4. Search Domain and Meta Data...................... 110 5.2.5. Guidage Algorithm............................. 112 5.2.6. Resynthesis.................................. 116 5.2.7. Sample Applications and Results.................... 117 5.2.8. Discussions.................................. 127 III How to Expect 129 Chapter 6. Adaptive and Interactive Learning........................ 130 6.1. Introduction....................................... 131 6.2. Background on Stochastic Music Modeling.................. 133 6.2.1. Memory Models............................... 134 6.2.2. Approaches to Statistical Learning................... 139 6.2.3. Approaches to Planning and Interaction................ 140 6.3. General Discussions................................. 143 6.4. Active Learning Architecture............................ 145 6.4.1. Audio Oracles for Memory Models................... 149 6.4.2. Guidage for Active Selection....................... 152 6.5. Anticipatory Learning................................ 154 6.5.1. Competitive and Collaborative learning................ 155 6.5.2. Memory-based Learning.......................... 157 6.6. Active Learning Algorithm............................. 158 6.6.1. Model Complexity............................. 160 6.7. Results and Experiments.............................. 161 6.7.1. Knowledge-Based Interactions...................... 162 6.7.2. Anticipatory Style Imitation and Automatic Improvisation... 168 6.8. Discussions....................................... 174 IV When to Expect 176 viii Chapter 7. Anticipatory Synchronization........................... 177 7.1. Introduction....................................... 178 7.2. Background....................................... 180 7.2.1. Score Following Research......................... 180 7.2.2. Cognitive Foundations of Musical Time................ 182 7.2.3. Compositional Foundations of Time.................. 183 7.2.4. Probabilistic Models of Time....................... 185 7.3. General Framework.................................. 188 7.3.1. Anticipatory Multimodal Inference................... 189 7.3.2. Hybrid Models of Time.......................... 190 7.4. Inference Formulation................................ 192 7.5. Stochastic model of time in music performance................ 194 7.5.1. Attentional Model of Tempo....................... 194 7.5.2. Tempo Agent and Decoding....................... 198 7.5.3. Survival Distribution Model....................... 200 7.6. Music Score Model.................................. 201 7.6.1. Basic Events................................. 201 7.6.2. Special timed events............................ 202 7.7. Observation Model.................................. 205 7.8. Evaluation........................................ 207 7.8.1. Evaluation of Tempo Prediction..................... 208 7.8.2. Evaluation over synthesized audio from score............ 209 7.8.3. Evaluation of real-time Alignment................... 217 7.9. Discussions....................................... 220 Chapter 8. Towards Writing of Time and Interaction in Computer Music...... 221 8.1. Background....................................... 223 8.1.1. Computer Music Language Paradigms................. 223 8.1.2. Practical Status...............................
Load more

Recommended publications
  • Expectancy and Musical Emotion Effects of Pitch and Timing
    Manuscript Expectancy and musical emotion Effects of pitch and timing expectancy on musical emotion Sauvé, S. A.1, Sayed, A.1, Dean, R. T.2, Pearce, M. T.1 1Queen Mary, University of London 2Western Sydney University Author Note Correspondence can be addressed to Sarah Sauvé at [email protected] School of Electronic Engineering and Computer Science Queen Mary University of London, Mile End Road London E1 4NS United Kingdom +447733661107 Biographies S Sauve: Originally a pianist, Sarah is now a PhD candidate in the Electronic Engineering and Computer Science department at Queen Mary University of London studying expectancy and stream segregation, supported by a college studentship. EXPECTANCY AND MUSICAL EMOTION 2 A Sayed: Aminah completed her MSc in Computer Science at Queen Mary University of London, specializing in multimedia. R.T. Dean: Roger is a composer/improviser and researcher at the MARCS Institute for Brain, Behaviour and Development. His research focuses on music cognition and music computation, both analytic and generative. M.T. Pearce: Marcus is Senior Lecturer at Queen Mary University of London, director of the Music Cognition and EEG Labs and co-director of the Centre for Mind in Society. His research interests cover computational, psychological and neuroscientific aspects of music cognition, with a particular focus on dynamic, predictive processing of melodic, rhythmic and harmonic structure, and its impact on emotional and aesthetic experience. He is the author of the IDyOM model of auditory expectation based on statistical learning and probabilistic prediction. EXPECTANCY AND MUSICAL EMOTION 3 Abstract Pitch and timing information work hand in hand to create a coherent piece of music; but what happens when this information goes against the norm? Relationships between musical expectancy and emotional responses were investigated in a study conducted with 40 participants: 20 musicians and 20 non-musicians.
    [Show full text]
  • A New Illusion in the Perception of Relative Pitch Intervals
    A new illusion in the perception of relative pitch intervals by Maartje Koning A thesis submitted to the Faculty of Humanities of the University of Amsterdam in partial fulfilment of the requirements for the degree of Master of Arts Department of Musicology 2015 Dr. M. Sadakata University of Amsterdam Dr. J.A. Burgoyne University of Amsterdam 2 Abstract This study is about the perception of relative pitch intervals. An earlier study of Sadakata & Ohgushi ‘Comparative judgments pitch intervals and an illusion’ (2000) showed that when when people listened to two tone intervals, their perception of relative pitch distance between the two tones depended on the direction and size of the intervals. In this follow-up study the participants had to listen to two tone intervals and indicate whether the size of the second interval was smaller, the same or larger than the first. The conditions were the same as in the study of Sadakata & Ohgushi. These four different conditions were illustrating the relationship between those two intervals. There were ascending and descending intervals and the starting tone of the second interval differed with respect to the starting tone of the first interval. The study made use of small and large intervals and hypothesized that the starting tone of the second interval with respect to the starting tone of the first interval had an effect on the melodic expectancy of the listener and because of that they over- or underestimate the size of the second tone interval. Furthermore, it was predicted that this tendency would be stronger for larger tone intervals compared to smaller tone intervals and that there would be no difference found between musicians and non-musicians.
    [Show full text]
  • Prediction in Polyphony: Modelling Musical Auditory Scene Analysis
    Prediction in polyphony: modelling musical auditory scene analysis by Sarah A. Sauvé A thesis submitted to the University of London for the degree of Doctor of Philosophy School of Electronic Engineering & Computer Science Queen Mary University of London United Kingdom September 2017 Statement of Originality I, Sarah A Sauvé, confirm that the research included within this thesis is my own work or that where it has been carried out in collaboration with, or supported by others, that this is duly acknowledged below and my contribution indicated. Previously published material is also acknowledged below. I attest that I have exercised reasonable care to ensure that the work is original, and does not to the best of my knowledge break any UK law, infringe any third party's copyright or other Intellectual Property Right, or contain any confidential material. I accept that the College has the right to use plagiarism detection software to check the electronic version of the thesis. I confirm that this thesis has not been previously submitted for the award of a degree by this or any other university. The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the author. Signature: Sarah A Sauvé Date: 1 September 2017 2 Details of collaboration and publication One journal article currently in review and one paper uploaded to the ArXiv database contain work presented in this thesis. Two conference proceedings papers contain work highly related to, and fundamental to the development of the work presented in Chapters 5 and 7.
    [Show full text]
  • Probabilistic Models of Expectation Violation Predict Psychophysiological Emotional Responses to Live Concert Music
    Probabilistic models of expectation violation predict psychophysiological emotional responses to live concert music Hauke Egermann, Marcus T. Pearce, Geraint A. Wiggins & Stephen McAdams Cognitive, Affective, & Behavioral Neuroscience ISSN 1530-7026 Cogn Affect Behav Neurosci DOI 10.3758/s13415-013-0161-y 1 23 Your article is protected by copyright and all rights are held exclusively by Psychonomic Society, Inc.. This e-offprint is for personal use only and shall not be self-archived in electronic repositories. If you wish to self-archive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com”. 1 23 Author's personal copy Cogn Affect Behav Neurosci DOI 10.3758/s13415-013-0161-y Probabilistic models of expectation violation predict psychophysiological emotional responses to live concert music Hauke Egermann & Marcus T. Pearce & Geraint A. Wiggins & Stephen McAdams # Psychonomic Society, Inc. 2013 Abstract We present the results of a study testing the often- emotion induction, leading to a further understanding of the theorized role of musical expectations in inducing listeners’ frequently experienced emotional effects of music. emotions in a live flute concert experiment with 50 participants. Using an audience response system developed for this purpose, Keywords Emotion .
    [Show full text]
  • When the Leading Tone Doesn't Lead: Musical Qualia in Context
    When the Leading Tone Doesn't Lead: Musical Qualia in Context Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Claire Arthur, B.Mus., M.A. Graduate Program in Music The Ohio State University 2016 Dissertation Committee: David Huron, Advisor David Clampitt Anna Gawboy c Copyright by Claire Arthur 2016 Abstract An empirical investigation is made of musical qualia in context. Specifically, scale-degree qualia are evaluated in relation to a local harmonic context, and rhythm qualia are evaluated in relation to a metrical context. After reviewing some of the philosophical background on qualia, and briefly reviewing some theories of musical qualia, three studies are presented. The first builds on Huron's (2006) theory of statistical or implicit learning and melodic probability as significant contributors to musical qualia. Prior statistical models of melodic expectation have focused on the distribution of pitches in melodies, or on their first-order likelihoods as predictors of melodic continuation. Since most Western music is non-monophonic, this first study investigates whether melodic probabilities are altered when the underlying harmonic accompaniment is taken into consideration. This project was carried out by building and analyzing a corpus of classical music containing harmonic analyses. Analysis of the data found that harmony was a significant predictor of scale-degree continuation. In addition, two experiments were carried out to test the perceptual effects of context on musical qualia. In the first experiment participants rated the perceived qualia of individual scale-degrees following various common four-chord progressions that each ended with a different harmony.
    [Show full text]
  • Surprise: a Belief Or an Emotion? in V.S
    Provided for non-commercial research and educational use only. Not for reproduction, distribution or commercial use. This chapter was originally published in the book Progress in Brain Research, Vol. 202 published by Elsevier, and the attached copy is provided by Elsevier for the author's benefit and for the benefit of the author's institution, for non-commercial research and educational use including without limitation use in instruction at your institution, sending it to specific colleagues who know you, and providing a copy to your institution’s administrator. All other uses, reproduction and distribution, including without limitation commercial reprints, selling or licensing copies or access, or posting on open internet sites, your personal or institution’s website or repository, are prohibited. For exceptions, permission may be sought for such use through Elsevier's permissions site at: http://www.elsevier.com/locate/permissionusematerial From: Barbara Mellers, Katrina Fincher, Caitlin Drummond and Michelle Bigony, Surprise: A belief or an emotion? In V.S. Chandrasekhar Pammi and Narayanan Srinivasan, editors: Progress in Brain Research, Vol. 202, Amsterdam: The Netherlands, 2013, pp. 3-19. ISBN: 978-0-444-62604-2 © Copyright 2013 Elsevier B.V. Elsevier Author's personal copy CHAPTER Surprise: A belief or an emotion? 1 Barbara Mellers1, Katrina Fincher, Caitlin Drummond, Michelle Bigony Department of Psychology, Solomon Labs, University of Pennsylvania, Philadelphia, PA, USA 1Corresponding author. Tel.: þ1-215-7466540, Fax: þ1-215-8987301, e-mail address: [email protected] Abstract Surprise is a fundamental link between cognition and emotion. It is shaped by cognitive as- sessments of likelihood, intuition, and superstition, and it in turn shapes hedonic experiences.
    [Show full text]
  • Auditory Expectation: the Information Dynamics of Music Perception and Cognition
    Topics in Cognitive Science 4 (2012) 625–652 Copyright Ó 2012 Cognitive Science Society, Inc. All rights reserved. ISSN: 1756-8757 print / 1756-8765 online DOI: 10.1111/j.1756-8765.2012.01214.x Auditory Expectation: The Information Dynamics of Music Perception and Cognition Marcus T. Pearce and Geraint A. Wiggins Centre for Cognition, Computation and Culture, Goldsmiths, University of London Centre for Digital Music, Queen Mary, University of London Received 30 September 2010; received in revision form 23 June 2011; accepted 25 July 2011 Abstract Following in a psychological and musicological tradition beginning with Leonard Meyer, and continuing through David Huron, we present a functional, cognitive account of the phenomenon of expectation in music, grounded in computational, probabilistic modeling. We summarize a range of evidence for this approach, from psychology, neuroscience, musicology, linguistics, and creativity studies, and argue that simulating expectation is an important part of understanding a broad range of human faculties, in music and beyond. Keywords: Expectation; Probabilistic modeling; Prediction; Musical melody; Pitch; Segmentation; Aesthetics; Creativity 1. Introduction Once a musical style has become part of the habit responses of composers, performers, and practiced listeners, it may be regarded as a complex system of probabilities … Out of such internalized probability systems arise the expectations—the tendencies—upon which musical meaning is built. (Meyer, 1957, p. 414) The ability to anticipate the future is a fundamental property of the human brain (Dennett, 1991). Expectations play a role in a multitude of cognitive processes from sensory percep- tion, through learning and memory, to motor responses and emotion generation. Accurate expectations allow organisms to respond to environmental events faster and more appropri- ately and to identify incomplete or ambiguous perceptual input.
    [Show full text]
  • The Pygmalion Effect: Proving Them Right
    The Pygmalion Effect: Proving Them Right getpocket.com/explore/item/the-pygmalion-effect-proving-them-right The Pygmalion Effect is a powerful secret weapon. Without even realizing it, we can nudge others towards success. In this article, discover how expectations can influence performance for better or worse. How Expectations Influence Performance Many people believe that their pets or children are of unusual intelligence or can understand everything they say. Some people have stories of abnormal feats. In the late 19th century, one man claimed that about his horse and appeared to have evidence. William Von Osten was a teacher and horse trainer. He believed that animals could learn to read or count. Von Osten’s initial attempts with dogs and a bear were unsuccessful, but when he began working with an unusual horse, he changed our understanding of psychology. Known as Clever Hans, the animal could answer questions, with 90% accuracy, by tapping his hoof. He could add, subtract, multiply, divide, and tell the time and the date. Clever Hans could also read and understand questions written or asked in German. Crowds flocked to see the horse, and the scientific community soon grew interested. Researchers 1/7 studied the horse, looking for signs of trickery. Yet they found none. The horse could answer questions asked by anyone, even if Von Osten was absent. This indicated that no signaling was at play. For a while, the world believed the horse was truly clever. Then psychologist Oskar Pfungst turned his attention to Clever Hans. Assisted by a team of researchers, he uncovered two anomalies.
    [Show full text]
  • Overcoming Variability in Consumer Response to Advertising Music
    Results May Vary: Overcoming Variability in Consumer Response to Advertising Music Lincoln G. Craton and Geoffrey P. Lantos Stonehill College Richard C. Leventhal Ashford University ABSTRACT Although listening to music seems effortless, it actually involves many separate psychological mechanisms. This article describes and extends the multimechanism framework proposed by Juslin and colleagues, highlighting how the operation of psychological mechanisms leads to two general types of variability in consumer response to advertising music. First, the risk of between-consumer variability (individual differences) in musical response is moderate or high for most mechanisms, and it often depends on each individual’s particular history of exposure to music (listening biography). Second, within-consumer variability occurs when different mechanisms have contrasting effects, so that an individual consumer’s musical response is often mixed (e.g., guilty pleasures, bittersweet feelings, pleasurable sadness). Both types of variability can negatively impact advertising objectives (message reception, recall, acceptance, brand attitudes, etc.). The article offers preliminary suggestions for how marketers can use a multimechanism approach to successfully incorporate music in commercials and reduce the risk of unanticipated consumer responses. It ends with proposals for further research. © 2016 Wiley Periodicals, Inc. Business is booming in the field of music percep- Scope and Goals of the Article tion and cognition (hereafter, music cognition). Re- search activity is surging (Levitin, 2010), member- According to Levitin and Tirovolas (2010, p. 599), ship in the Society for Music Perception and Cognition (SMPC) is rising, and “after a long drought” (Ashley, Music cognition . is the scientific study of those 2010, p. 205) new introductory textbooks are emerg- mental and neural operations underlying music lis- ing (e.g., Honing, 2009; Tan, Pfordrescher, & Harre´ tening, music making, dancing (moving to music), 2010; Thompson, 2015).
    [Show full text]
  • Cortical Encoding of Melodic Expectations in Human
    RESEARCH ARTICLE Cortical encoding of melodic expectations in human temporal cortex Giovanni M Di Liberto1*, Claire Pelofi2,3†, Roberta Bianco4†, Prachi Patel5,6, Ashesh D Mehta7,8, Jose L Herrero7,8, Alain de Cheveigne´ 1,4, Shihab Shamma1,9*, Nima Mesgarani5,6* 1Laboratoire des syste`mes perceptifs, De´partement d’e´tudes cognitives, E´ cole normale supe´rieure, PSL University, CNRS, 75005 Paris, France; 2Department of Psychology, New York University, New York, United States; 3Institut de Neurosciences des Syste`me, UMR S 1106, INSERM, Aix Marseille Universite´, Marseille, France; 4UCL Ear Institute, London, United Kingdom; 5Department of Electrical Engineering, Columbia University, New York, United States; 6Mortimer B Zuckerman Mind Brain Behavior Institute, Columbia University, New York, United States; 7Department of Neurosurgery, Zucker School of Medicine at Hofstra/ Northwell, Manhasset, United States; 8Feinstein Institute of Medical Research, Northwell Health, Manhasset, United States; 9Institute for Systems Research, Electrical and Computer Engineering, University of Maryland, College Park, United States Abstract Humans engagement in music rests on underlying elements such as the listeners’ cultural background and interest in music. These factors modulate how listeners anticipate musical events, a process inducing instantaneous neural responses as the music confronts these *For correspondence: expectations. Measuring such neural correlates would represent a direct window into high-level [email protected] (GMDL); brain processing. Here we recorded cortical signals as participants listened to Bach melodies. We [email protected] (SS); [email protected] (NM) assessed the relative contributions of acoustic versus melodic components of the music to the neural signal. Melodic features included information on pitch progressions and their tempo, which † These authors contributed were extracted from a predictive model of musical structure based on Markov chains.
    [Show full text]
  • The Processing of Pitch and Temporal Information in Relational Memory for Melodies
    The Processing of Pitch and Temporal Information in Relational Memory for Melodies Timothy Patrick Byron Bachelor of Arts (Psychology) (Honours) A thesis submitted for the degree of Doctor of Philosophy University of estern Sydney March 2008 i I hereby declare that this submission is my own work and, to the best of my knowledge, it contains no material previously published or written by another person, nor material which has been accepted for the award of any other degree or diploma at the University of estern Sydney, or any other educational institution, e)cept where due acknowledgement is made in the thesis. I also declare that the intellectual content of this thesis is the product of my own work, e)cept to the e)tent that assistance from others in the project,s design and conception is acknowledged. _____________________ Tim Byron . ii Acknowledgements .ou would not currently be reading this thesis without the assistance of the following people, who provided wisdom, encouragement, generosity, insight, and motivation. Thanks are very much due to my primary supervisor, Assistant Professor Catherine Stevens, has been unfailing in her dedication and commitment as a supervisor for close to four years, from the first days in which I was able to begin researching this thesis in earnest, to 0 especially 1 those dying days when the world seems surreal. The ways in which 2ate has contributed to this thesis are boundless, but her patience, encouragement, motivational skills, intellectual curiosity, critical insight, good humour, astute observations, and her belief in me all come to mind. This thesis does not resemble the thesis proposal I submitted in late 2003, and I am eternally grateful to 2ate for the knowledge, insight, patience and guidance she gave me as my initially incoherent ideas and fancies eventually got pruned away, leaving the shape of a coherent thesis.
    [Show full text]
  • The Cognitive Neuroscience of Music
    THE COGNITIVE NEUROSCIENCE OF MUSIC Isabelle Peretz Robert J. Zatorre Editors OXFORD UNIVERSITY PRESS Zat-fm.qxd 6/5/03 11:16 PM Page i THE COGNITIVE NEUROSCIENCE OF MUSIC This page intentionally left blank THE COGNITIVE NEUROSCIENCE OF MUSIC Edited by ISABELLE PERETZ Départment de Psychologie, Université de Montréal, C.P. 6128, Succ. Centre-Ville, Montréal, Québec, H3C 3J7, Canada and ROBERT J. ZATORRE Montreal Neurological Institute, McGill University, Montreal, Quebec, H3A 2B4, Canada 1 Zat-fm.qxd 6/5/03 11:16 PM Page iv 1 Great Clarendon Street, Oxford Oxford University Press is a department of the University of Oxford. It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide in Oxford New York Auckland Bangkok Buenos Aires Cape Town Chennai Dar es Salaam Delhi Hong Kong Istanbul Karachi Kolkata Kuala Lumpur Madrid Melbourne Mexico City Mumbai Nairobi São Paulo Shanghai Taipei Tokyo Toronto Oxford is a registered trade mark of Oxford University Press in the UK and in certain other countries Published in the United States by Oxford University Press Inc., New York © The New York Academy of Sciences, Chapters 1–7, 9–20, and 22–8, and Oxford University Press, Chapters 8 and 21. Most of the materials in this book originally appeared in The Biological Foundations of Music, published as Volume 930 of the Annals of the New York Academy of Sciences, June 2001 (ISBN 1-57331-306-8). This book is an expanded version of the original Annals volume. The moral rights of the author have been asserted Database right Oxford University Press (maker) First published 2003 All rights reserved.
    [Show full text]