DOCSLIB.ORG

Taking Harmony Into Account: the Effect of Harmony on Melodic Probability

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Taking Harmony Into Account: the Effect of Harmony on Melodic Probability The Effect of Harmony on Melodic Probability 405 TAKING HARMONY INTO ACCOUNT: THE EFFECT OF HARMONY ON MELODIC PROBABILITY CLAIRE ARTHUR musical examples, as well as acute musical intuitions. Ohio State University A testament to the influence of these theories is the large body of work that has attempted to simplify, quantify, PROBABILISTIC MODELS HAVE PROVED REMARKABLY test, or otherwise expand upon them, most notably: successful in modeling melodic organization (e.g., Cuddy and Lunney (1995), Krumhansl (1995), Larson Huron, 2006a; Pearce, 2005; Temperley, 2008). However, (2004), Lerdahl (2001), Margulis (2005), Pearce and the majority of these models rely on pitch information Wiggins (2006), and Schellenberg (1996, 1997). (For takenfrommelodyalone.Giventheprevalenceof a review of expectation literature, see Pearce & Wiggins, homophonic music in Western culture, however, sur- 2006; Temperley, 2012.) The majority of these models prisingly little attention has been directed at exploring focus on the evaluation of pitch information taken from the predictive power of harmonic accompaniment in the melody alone (e.g., pitch height, interval size, con- models of melodic organization. The research presented tour, etc.). However, while the pitch domain has here uses a combination of the three main approaches received the greatest attention, research has also estab- to empirical musicology—exploratory analysis, model- lished the importance of rhythm and phrasing in con- ing, and hypothesis testing—to investigate the influence tributing to melodic expectation. For example, rhythmic of harmony on melodic behavior. In this study a com- information can help predict the location of phrase end- parison is made between models that use only melodic ings (Jusczyk & Krumhanl, 1993, Krumhansl, 2000; information and models that consider the melodic Krumhansl & Jusczyk, 1990; Palmer & Krumhansl, information along with the underlying harmonic 1987), and pitches located near phrase endings will have accompaniment to predict melodic continuations. A test an increased probability to move towards their note of of overall performance shows a significant improve- resolution (Aarden, 2003; Pearce, 2005). Given the prev- ment using a melodic-harmonic model. When individ- alence of homophonic music (i.e., melody with accom- ual scale degrees are examined, the major diatonic scale paniment) in Western musical cultures, an obvious degrees are shown to have unique probability distribu- avenue for further exploration in models of melodic tions for each of their most common harmonic settings. organization would be that of harmonic context. In That is, the results suggest a robust effect of harmony on other words, in modeling melodic expectancy, it may melodic organization. Perceptual implications and prove beneficial to examine melody not just as isolated directions for future research are discussed. lines, but as lines embedded in a harmonic context. While some scholars have considered the effects of har- Received: June 5, 2015, accepted August 20, 2016. mony on musical expectations in experimental settings, Key words: melodic probability, melodic modeling, it has typically been investigated in isolation, such as melodic expectation, harmony, scale-degree distributions studies examining the ‘‘perceived relatedness’’ of har- monies (Krumhansl, Bharucha, & Kessler, 1982; Bhar- ucha & Krumhansl, 1983; Bharucha & Stoeckig, 1986; USICAL EXPECTANCY IS A FREQUENT TOPIC Bharucha, 1987). One paper that has investigated the of interest in the music theory and music expectations of melody and harmony combined is that M perception literature. Many models of of Schmuckler (1989). Schmuckler’s results are some- melodic expectancy have been proposed over the past what counterintuitive; based on the results from three several decades. Two early theoretical models in partic- studies he proposes that melody and harmony have ular have been highly influential to the fields of music independent effects on musical expectations. However, theory and psychology, specifically those of Meyer as will be further discussed in the conclusion, Schmuck- (1956) and Narmour (1992). Each of these models ler’s unusual finding was never replicated. is highly complex and comprehensive in scope, with Although the processes underlying expectation are insights drawn from observations of hundreds of complex and multifaceted, research has suggested that Music Perception, VOLUME 34, ISSUE 4, PP. 405–423, ISSN 0730-7829, ELECTRONIC ISSN 1533-8312. © 2017 BY THE REGENTS OF THE UNIVERSITY OF CALIFORNIA ALL RIGHTS RESERVED. PLEASE DIRECT ALL REQUESTS FOR PERMISSION TO PHOTOCOPY OR REPRODUCE ARTICLE CONTENT THROUGH THE UNIVERSITY OF CALIFORNIA PRESS’S REPRINTS AND PERMISSIONS WEB PAGE, HTTP://WWW.UCPRESS.EDU/JOURNALS.PHP?P¼REPRINTS. DOI: https://doi.org/10.1525/MP.2017.34.4.405 406 Claire Arthur statistical learning plays a substantial role in forming the causal influence between melody and harmony can musical expectations (e.g., Huron, 2006a; Krumhansl, go in both directions: That is, melody might be expected 1990; Pearce, 2005; Pearce & Wiggins, 2006; Temperley, to affect harmony as well as harmony affecting melody. 2007). Given that implicit knowledge can arise from For the purposes of this paper, however, only the prob- probabilistic exposure to sequences and patterns abilistic relationship of harmony on melody will be (Romberg & Saffran, 2010; Saffran, Johnson, Aslin, & investigated, leaving aside the reverse analysis for Newport, 1999), it would be beneficial for researchers another occasion. interested in studying melodic expectancy to under- There are many ways to quantify and dissect melodic stand how various musical features contribute to the information. Many models—in particular those based statistical properties of melody. (For a review of implicit on Narmour’s—tally not only pitch information, but the and probabilistic learning see Reber, 1993.) specific interval and direction from pitch to pitch. The A small but growing body of research has investigated current model is based on ideas of statistical learning harmony from a probabilistic perspective, typically with proposed by Huron (2006a) where only first-order the goal of informing Music Information Retrieval scale-degree information is considered. Using scale (MIR) efforts, such as feature extraction or automatic degree, like pitch class, assumes octave equivalence and music generation (e.g., Ponsford, Wiggins, & Mellish, removes information about the size and direction of an 1999; Quinn & White, 2013; Raphael & Stoddard, interval. This means that the model does not distinguish 2004). Other research taking a probabilistic approach between, for instance, a major second and a minor to harmony includes the work of White (2013), who seventh. However, one should bear in mind that the used hidden Markov models to search for harmonic possible interval and direction between two scale transition information in a large corpus with the aim degrees has only two options (ignoring compound of comparing stylistic and historical trends. White does intervals, which are extremely rare in melodies), and not report the transitional probabilities or the distribu- it is well known that that small melodic intervals are tion of harmonies in the corpus, instead using them as much more common than large ones (Dowling, 1967; hidden factors in his analyses. Similarly, Rohrmeier and Huron, 2001; Ortmann, 1926; Merriam, Whinery & Cross (2008) look at statistical properties of harmony, Fred, 1956; Temperley, 2008). Thus, most scale-degree taking similar aims to the present paper in attempting to successions will represent the smaller of the two inter- provide ‘‘empirical evidence to clarify common assump- vallic possibilities. tions concerning harmony and its perception,’’ and to As will be discussed in further detail in the following ‘‘ground’’ categorizations of harmony found in music sections, ‘‘harmonic information’’ in this case refers to perception literature via a statistical perspective. How- a reduction of the accompanimental texture to a Roman ever, their work only considers the harmonic progres- numeral. This approach has several limitations that sions of the Bach chorales, and like the other harmonic should be acknowledged, such as the reliance on human studies mentioned above, they do not look at melodic (subjective) analysis, the marginalization of voice- probability. leading practices, and the insertion of a ‘‘presentist’’ bias In this paper the role of the supporting harmonic which discards relevant stylistic information (Gjerdin- accompaniment in shaping melodic organization gen, 2014). In this case, using Roman numerals repre- is investigated using a corpus-based, probabilistic sented the simplest method that would generate the approach. A model is constructed that combines har- fewest comparisons, and provided the simplest strategy monic information from the accompaniment with first- for analyzing homophonic music. order melodic information using a digital corpus of It should be stated that this model of melodic proba- encoded musical scores from the common practice bility is not being forwarded as an optimal model of period. This melodic/harmonic model is compared to melodic prediction. Any realistic model of melodic a model that uses solely first-order melodic information expectancy will certainly consider more than first- in order to investigate the effect of harmony on the order scale-degree successions and a crude harmonic predictability of
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]
  • 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]
  • 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]
  • Semiótica, Semiótica De La Música Y Semiótica Cognitivo- Enactiva De La Música Notas Para Un Manual De Usuario
    Semiótica, semiótica de la música y semiótica cognitivo- enactiva de la música Notas para un manual de usuario. Rubén López Cano [email protected], www.lopezcano.net Escola Superior de Música de Catalunya Rubén López Cano 2007 Los contenidos de este texto están bajo una licencia Creative Commons. Consúltela antes de usarlo. The content on this text is under a Creative Commons license. Consult it before using this article. Cómo citar este artículo: How to cite this article: López Cano, Rubén. 2007. “Semiótica, semiótica de la música y semiótica cognitivo-enactiva de la música. Notas para un manual de usuario”. Texto didáctico (actualizado junio 2007). www.lopezcano.net (Consultado o descargado [día, mes y año]) (Accessed [Day Month Year of access]) Semiótica de la música Rubén López Cano Índice: • Advertencia • Semiótica de la Música • El Signo • ¿Qué es significado en música? • Fundamentos • Saussure y Peirce • Semiótica cognitiva de la música • ¿Semiología o semiótica? • Persuasiones y disuasiones: oferta y problemas de la semiótica de la música • Las ciencias cognitivas • La musicología cognitiva • La cognición enactiva • Como introducirse en el estudio de esto… o Semiótica de la música Algunos autores importantes en el ámbito de la semiótica de la música: Primer encontronazo con la semiótica Introducción a la semiótica general Semiótica de Peirce Semióticas particulares: Eco y Greimas Historia de la semiótica musical Introducción a la semiótica de la música Bibliografia fundamental de algunos maestros de la semiótica musical
    [Show full text]
  • Dynamic Melodic Expectancy
    DYNAMIC MELODIC EXPECTANCY DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Bret J. Aarden, M.A. The Ohio State University 2003 Dissertation Committee: Approved by Professor David Huron, Adviser Professor Patricia Flowers Adviser Professor Mari Riess Jones School of Music Professor Lee Potter ABSTRACT The most common method for measuring melodic expectancy is the “probe-tone” design, which relies on a retrospective report of expectancy. Here a direct measure of expectancy is introduced, one that uses a speeded, serial categorization task. An analysis of the reaction time data showed that “Implication-Realization” contour models of melodic expectancy provide a good fit. Further analysis suggests that some assumptions of these contour models may not be valid. The traditional “key profile” model of tonality was not found to contribute significantly to the model. Following Krumhansl’s (1990) argument that tonality is learned from the statistical distribution of scale degrees, a tonality model based on the actual probability of scale degrees did significantly improve the fit of the model. It is proposed that the probe-tone method for measuring key profiles encourages listeners to treat the probe tone as being in phrase-final position. Indeed, the key profile was found to be much more similar to the distribution of phrase-final notes than to the distribution of all melodic notes. A second experiment measured reaction times to notes that subjects expected to be phrase-final. In this experiment the key profile contributed significantly to the fit of the model.
    [Show full text]