Computational Models of Expressive Music Performance: A Comprehensive and Critical Review

Expressive performance is an indispensable part of music making. When playing a piece, expert performers shape various parameters (tempo, timing, dynamics, intonation, articulation, etc.) in ways that are not prescribed by the notated score, in this way producing an expressive rendition that brings out dramatic, affective, and emotional qualities that may engage and affect the listeners. Given the central importance of this skill for many kinds of music, expressive performance has become an important research topic for disciplines like musicology, music psychology, etc. This paper focuses on a specific thread of research: work on computational music performance models. Computational models are attempts at codifying hypotheses about expressive performance in terms of mathematical formulas or computer programs, so that they can be evaluated in systematic and quantitative ways. Such models can serve at least two main purposes: they permit us to systematically study certain hypotheses regarding performance; and they can be used as tools to generate automated or semi-automated performances, in artistic or educational contexts. The present article presents an up-to-date overview of the state of the art in this domain. We explore recent trends in the field, such as a strong focus on data-driven (machine learning); a growing interest in interactive expressive systems, such as conductor simulators and automatic accompaniment systems; and an increased interest in exploring cognitively plausible features and models. We provide an in-depth discussion of several important design choices in such computer models, and discuss a crucial (and still largely unsolved) problem that is hindering systematic progress: the question of how to evaluate such models in scientifically and musically meaningful ways. From all this, we finally derive some research directions that should be pursued with priority, in order to advance the field and our understanding of expressive music performance.

[1]  D. Moelants,et al.  Exploring the effect of tempo changes on violinists’ body movements , 2019 .

[2]  Maarten Grachten,et al.  A Computational Study of the Role of Tonal Tension in Expressive Piano Performance , 2018, ArXiv.

[3]  Dominic McIver Lopes,et al.  Hearing and Seeing Musical Expression , 2009 .

[4]  W. Goebl,et al.  Communication for coordination: gesture kinematics and conventionality affect synchronization success in piano duos , 2017, Psychological Research.

[5]  W. Goebl,et al.  Beating time: How ensemble musicians’ cueing gestures communicate beat position and tempo , 2017, Psychology of music.

[6]  Katerina Kosta,et al.  Mapping between dynamic markings and performed loudness: a machine learning approach , 2016, Machine Learning and Music Generation.

[7]  Sergio Giraldo,et al.  A machine learning approach to ornamentation modeling and synthesis in jazz guitar , 2016, Machine Learning and Music Generation.

[8]  Martin Bonev,et al.  The ACCompanion v0.1: An Expressive Accompaniment System , 2017, ArXiv.

[9]  Mark D. Plumbley,et al.  Clustering Expressive Timing with Regressed Polynomial Coefficients Demonstrated by a Model Selection Test , 2017, ISMIR.

[10]  Ching-Hua Chuan,et al.  A Functional Taxonomy of Music Generation Systems , 2017, ACM Comput. Surv..

[11]  M. Leman,et al.  Introduction : What Is Embodied Music Interaction? , 2017 .

[12]  Gerhard Widmer,et al.  What were you expecting? Using Expectancy Features to Predict Expressive Performances of Classical Piano Music , 2017, ArXiv.

[13]  Marcelo M. Wanderley,et al.  Individuality in Piano Performance Depends on Skill Learning , 2017, MOCO.

[14]  Carlos Eduardo Cancino Chacón,et al.  Temporal Dependencies in the Expressive Timing of Classical Piano Performances , 2017 .

[15]  Anders Friberg,et al.  Predicting the perception of performed dynamics in music audio with ensemble learning. , 2017, The Journal of the Acoustical Society of America.

[16]  Wil M. P. van der Aalst,et al.  Business Process Variability Modeling , 2017, ACM Comput. Surv..

[17]  Gerhard Widmer,et al.  An evaluation of linear and non-linear models of expressive dynamics in classical piano and symphonic music , 2017, Machine Learning.

[18]  Sergio Canazza,et al.  Algorithms can Mimic Human Piano Performance: The Deep Blues of Music , 2017 .

[19]  Sarvapali D. Ramchurn,et al.  Algorithms for Graph-Constrained Coalition Formation in the Real World , 2017, TIST.

[20]  Gerhard Widmer,et al.  Toward Computer-Assisted Understanding of Dynamics in Symphonic Music , 2016, IEEE MultiMedia.

[21]  Gerhard Widmer,et al.  Getting Closer to the Essence of Music , 2016, ACM Trans. Intell. Syst. Technol..

[22]  Marc Leman,et al.  On the Role of the Hand in the Expression of Music , 2017, The Hand.

[23]  Sander Dieleman,et al.  Learning to Create Piano Performances , 2017 .

[24]  H. Katayose,et al.  CONSTRUCTING PEDB 2nd EDITION: A MUSIC PERFORMANCE DATABASE WITH PHRASE INFORMATION , 2017 .

[25]  Eita Nakamura,et al.  Performance Error Detection and Post-Processing for Fast and Accurate Symbolic Music Alignment , 2017, ISMIR.

[26]  Cynthia C. S. Liem,et al.  A Formalization of Relative Local Tempo Variations in Collections of Performances , 2017, ISMIR.

[27]  Sergio I. Giraldo,et al.  A Machine Learning Approach to Discover Rules for Expressive Performance Actions in Jazz Guitar Music , 2016, Front. Psychol..

[28]  François Pachet,et al.  Maximum entropy models for generation of expressive music , 2016, ArXiv.

[29]  S. McAdams,et al.  Analysis, Performance, and Tension Perception of an Unmeasured Prelude for Harpsichord , 2016 .

[30]  Álvaro Sarasúa,et al.  Becoming the Maestro - A Game to Enhance Curiosity for Classical Music , 2016, 2016 8th International Conference on Games and Virtual Worlds for Serious Applications (VS-GAMES).

[31]  Stefanie A. Wind,et al.  Examining Rater Precision in Music Performance Assessment: An Analysis of Rating Scale Structure Using the Multifaceted Rasch Partial Credit Model , 2016 .

[32]  Elaine Chew,et al.  Tension ribbons: Quantifying and visualising tonal tension. , 2016 .

[33]  Geraint A. Wiggins,et al.  Linking melodic expectation to expressive performance timing and perceived musical tension. , 2016, Journal of experimental psychology. Human perception and performance.

[34]  E. Chew Playing with the Edge: Tipping Points and the Role of Tonality , 2016 .

[35]  Carlos Eduardo Cancino-Chacón,et al.  The Basis Mixer : A Computational Romantic Pianist , 2016 .

[36]  Plumbley,et al.  A model selection test on effective factors of the choice of expressive timing clusters for a phrase , 2016 .

[37]  Matthias Abend Cognitive Foundations Of Musical Pitch , 2016 .

[38]  Rafael Ramírez,et al.  Jazz Ensemble Expressive Performance Modeling , 2016, ISMIR.

[39]  Eita Nakamura,et al.  Autoregressive Hidden Semi-Markov Model of Symbolic Music Performance for Score Following , 2015, ISMIR.

[40]  Roger B. Dannenberg,et al.  Spectral Learning for Expressive Interactive Ensemble Music Performance , 2015, ISMIR.

[41]  Alan Hanjalic,et al.  Comparative Analysis of Orchestral Performance Recordings: An Image-Based Approach , 2015, ISMIR.

[42]  Carlos Eduardo Cancino Chacón,et al.  An Evaluation of Score Descriptors Combined with Non-linear Models of Expressive Dynamics in Music , 2015, Discovery Science.

[43]  Markus Schedl,et al.  PHENICX: Innovating the classical music experience , 2015, 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW).

[44]  Kenneth Sörensen,et al.  Generating Fingerings for Polyphonic Piano Music with a Tabu Search Algorithm , 2015, MCM.

[45]  Elaine Chew,et al.  A Change-Point Approach Towards Representing Musical Dynamics , 2015, MCM.

[46]  Mark D. Plumbley,et al.  The Clustering of Expressive Timing Within a Phrase in Classical Piano Performances by Gaussian Mixture Models , 2015, CMMR.

[47]  Roger B. Dannenberg,et al.  Duet interaction: learning musicianship for automatic accompaniment , 2015, NIME.

[48]  Sergio Canazza,et al.  CaRo 2.0: An Interactive System for Expressive Music Rendering , 2015, Adv. Hum. Comput. Interact..

[49]  D. Moelants,et al.  The influence of tempo on expressive timing: a multimodal approach , 2015 .

[50]  Larry A. Wasserman,et al.  A Statistical View on the Expressive Timing of Piano Rolled Chords , 2015, ISMIR.

[51]  Sergio Canazza,et al.  The Role of Individual Difference in Judging Expressiveness of Computer-Assisted Music Performances by Experts , 2014, ACM Trans. Appl. Percept..

[52]  Peter E. Keller,et al.  A conceptual review on action-perception coupling in the musicians’ brain: what is it good for? , 2014, Front. Hum. Neurosci..

[53]  Rafael Ramirez,et al.  The Sense of Ensemble: a Machine Learning Approach to Expressive Performance Modelling in String Quartets , 2014 .

[54]  Roger B. Dannenberg,et al.  Methods and Prospects for Human–Computer Performance of Popular Music , 2014, Computer Music Journal.

[55]  Hirokazu Kameoka,et al.  Mixture of Gaussian process experts for predicting sung melodic contour with expressive dynamic fluctuations , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[56]  Eita Nakamura,et al.  A Stochastic Temporal Model of Polyphonic MIDI Performance with Ornaments , 2014, ArXiv.

[57]  Yasuyuki Saito,et al.  Outer-Product Hidden Markov Model and Polyphonic MIDI Score Following , 2014, ArXiv.

[58]  Florian Krebs,et al.  An Assessment of Learned Score Features for Modeling Expressive Dynamics in Music , 2014, IEEE Transactions on Multimedia.

[59]  Elaine Chew,et al.  Practical Implications of Dynamic Markings in the Score: Is Piano Always Piano? , 2014, Semantic Audio.

[60]  Tadashi Kitamura,et al.  Laminae: A stochastic modeling-based autonomous performance rendering system that elucidates performer characteristics , 2014, ICMC.

[61]  Maarten Grachten,et al.  Predicting Expressive Dynamics in Piano Performances using Neural Networks , 2014, ISMIR.

[62]  Emery Schubert,et al.  Open ended descriptions of computer assisted interpretations of musical performance : An investigation of individual differences , 2014 .

[63]  Mark D. Plumbley,et al.  Evidence that phrase-level tempo variation may be represented using a limited dictionary , 2014 .

[64]  Anders Friberg,et al.  Using computational models of music performance to model stylistic variations , 2014 .

[65]  Sergio Canazza,et al.  Music Systemisers and Music Empathisers - Do they rate expressiveness of computer generated performances the same? , 2014, ICMC.

[66]  Anders Friberg,et al.  Software tools for automatic music performance , 2014 .

[67]  Eita Nakamura,et al.  Merged-Output HMM for Piano Fingering of Both Hands , 2014, ISMIR.

[68]  Chia-Jung Tsay Sight over sound in the judgment of music performance , 2013, Proceedings of the National Academy of Sciences.

[69]  Anders Friberg,et al.  Emotional expression in music: contribution, linearity, and additivity of primary musical cues , 2013, Front. Psychol..

[70]  Elaine Chew,et al.  Conceptual and Experiential Representations of Tempo: Effects on Expressive Performance Comparisons , 2013, MCM.

[71]  Satoru Fukayama,et al.  Statistical Approach to Automatic Expressive Rendition of Polyphonic Piano Music , 2013, Guide to Computing for Expressive Music Performance.

[72]  Alexis Kirke,et al.  An Overview of Computer Systems for Expressive Music Performance , 2013, Guide to Computing for Expressive Music Performance.

[73]  Atsuo Takanishi,et al.  Anthropomorphic Musical Robots Designed to Produce Physically Embodied Expressive Performances of Music , 2013, Guide to Computing for Expressive Music Performance.

[74]  Anders Friberg,et al.  Systems for Interactive Control of Computer Generated Music Performance , 2013, Guide to Computing for Expressive Music Performance.

[75]  Gerhard Widmer,et al.  Expressive Performance Rendering with Probabilistic Models , 2013, Guide to Computing for Expressive Music Performance.

[76]  Anders Friberg,et al.  Evaluation of Computer Systems for Expressive Music Performance , 2013, Guide to Computing for Expressive Music Performance.

[77]  Esteban Maestre,et al.  Investigating the relationship between expressivity and synchronization in ensemble performance: an exploratory study , 2013 .

[78]  Gerhard Widmer,et al.  Linear Basis Models for Prediction and Analysis of Musical Expression , 2012 .

[79]  Giovanni De Poli,et al.  On Evaluating Systems for Generating Expressive Music Performance: the Rencon Experience , 2012 .

[80]  Yann LeCun,et al.  Moving Beyond Feature Design: Deep Architectures and Automatic Feature Learning in Music Informatics , 2012, ISMIR.

[81]  Henri Ralambondrainy,et al.  Score Analyzer: Automatically Determining Scores Difficulty Level for Instrumental e-Learning , 2012, ISMIR.

[82]  Jean-Louis Giavitto,et al.  Correct Automatic Accompaniment Despite Machine listening or Human errors in Antescofo , 2012, ICMC.

[83]  Friedrich Platz,et al.  When the Eye Listens: A Meta-analysis of How Audio-visual Presentation Enhances the Appreciation of Music Performance , 2012 .

[84]  Elad Liebman,et al.  A Phylogenetic Approach to Music Performance Analysis , 2012 .

[85]  M. Farbood A Parametric, Temporal Model of Musical Tension , 2012 .

[86]  D. Moelants,et al.  The Influence of an Audience on Performers: A Comparison Between Rehearsal and Concert Using Audio, Video and Movement Data , 2012 .

[87]  Tetsuya Ogata,et al.  A Musical Robot that Synchronizes with a Coplayer Using Non-Verbal Cues , 2012, Adv. Robotics.

[88]  C. Raphael,et al.  Modeling Piano Interpretation Using Switching Kalman Filter , 2012, ISMIR.

[89]  G. Widmer,et al.  Expressive Performance Rendering with Probabilistic Model , 2012 .

[90]  Florian Krebs,et al.  Combining Score And Filter Based Models To Predict Tempo Fluctuations In Expressive Music Performances , 2012 .

[91]  Roberto Bresin,et al.  Emotion rendering in music: Range and characteristic values of seven musical variables , 2011, Cortex.

[92]  Guy Hoffman,et al.  Interactive improvisation with a robotic marimba player , 2011, Auton. Robots.

[93]  Alan Hanjalic,et al.  Expressivity in Musical Timing in Relation to Musical Structure and Interpretation: A Cross-Performance, Audio-Based Approach , 2011, Semantic Audio.

[94]  Caroline Palmer,et al.  Rate Effects on Timing, Key Velocity, and Finger Kinematics in Piano Performance , 2011, PloS one.

[95]  Satoru Fukayama,et al.  Polyhymnia: An Automatic Piano Performance System with Statistical Modeling of Polyphonic Expression and Musical Symbol Interpretation , 2011, NIME.

[96]  Marilyn Gail Boltz,et al.  Illusory Tempo Changes Due to Musical Characteristics , 2011 .

[97]  J. Sloboda,et al.  Handbook of Music and Emotion: Theory, Research, Applications , 2011 .

[98]  Marco Fabiani Interactive computer-aided expressive music performance : Analysis, control, modification and synthesis , 2011 .

[99]  Expressive Performance with Bayesian Networks and Linear Basis Models , 2011 .

[100]  Roger B. Dannenberg,et al.  Characterizing Tempo Change In Musical Performances , 2011, ICMC.

[101]  A. Friberg,et al.  An accent-based approach to performance rendering: Music theory meets music psychology , 2011 .

[102]  Tadashi Kitamura,et al.  Stochastic Modeling of a Musical Performance with Expressive Representations from the Musical Score , 2011, ISMIR.

[103]  Alan Hanjalic,et al.  Expressive Timing from Cross-Performance and Audio-based Alignment Patterns: An Extended Case Study , 2011, ISMIR.

[104]  Gerhard Widmer,et al.  The Magaloff Project: An Interim Report , 2010 .

[105]  Miguel Molina-Solana,et al.  Identifying violin performers by their expressive trends , 2010, Intell. Data Anal..

[106]  Marc R. Thompson,et al.  Embodied Meter: Hierarchical Eigenmodes in Music-Induced Movement , 2010 .

[107]  Christopher Raphael,et al.  Music Plus One and Machine Learning , 2010, ICML.

[108]  Haruhiro Katayose,et al.  "VirtualPhilharmony": A Conducting System with Heuristics of Conducting an Orchestra , 2010, NIME.

[109]  Geraint A. Wiggins,et al.  On the non-existence of music: Why music theory is a figment of the imagination , 2010 .

[110]  S. Sagayama,et al.  PERFORMANCE RENDERING FOR POLYPHONIC PIANO MUSIC WITH A COMBINATION OF PROBABILISTIC MODELS FOR MELODY AND HARMONY , 2010 .

[111]  A. Gabrielsson,et al.  The role of structure in the musical expression of emotions , 2010 .

[112]  Gerhard Widmer,et al.  Evidence for Pianist-specific Rubato Style in Chopin Nocturnes , 2010, ISMIR.

[113]  Christopher Raphael Symbolic and Structural Representation of Melodic Expression , 2009, ISMIR.

[114]  Gerhard Widmer,et al.  YQX Plays Chopin , 2009, AI Mag..

[115]  Gerhard Widmer,et al.  Phase-plane Representation and Visualization of Gestural Structure in Expressive Timing , 2009 .

[116]  C. Palmer,et al.  Synchronization of Timing and Motion 435 , 2022 .

[117]  J. Wapnick,et al.  Effects of Non-Musical Attributes and Excerpt Duration on Ratings of High-Level Piano Performances , 2009 .

[118]  G. Widmer,et al.  chapter 7 on the use of computational methods for expressive music Performance , 2009 .

[119]  Lijuan Peng,et al.  A Gestural Interface for Orchestral Conducting Education , 2009, CSEDU.

[120]  Gerhard Widmer,et al.  Who Is Who in the End? Recognizing Pianists by Their Final Ritardandi , 2009, ISMIR.

[121]  Eric Cheng,et al.  Quantitative Analysis of Phrasing Strategies in Expressive Performance: Computational Methods and Analysis of Performances of Unaccompanied Bach for Solo Violin , 2008 .

[122]  Arshia Cont,et al.  Antescofo: Anticipatory Synchronization and control of Interactive parameters in Computer Music , 2008, ICMC.

[123]  Shin-ichi Maeda,et al.  Gaussian Process Regression for Rendering Music Performance , 2008 .

[124]  Haruhiro Katayose,et al.  A New Music Database Describing Deviation Information of Performance Expressions , 2008, ISMIR.

[125]  Giovanni De Poli,et al.  Sense in expressive music performance: Data acquisition, computational studies, and models , 2008 .

[126]  Craig Stuart Sapp Hybrid Numeric/Rank Similarity Metrics for Musical Performance Analysis , 2008, ISMIR.

[127]  Miguel Molina-Solana,et al.  Using Expressive Trends for Identifying Violin P erformers , 2008, ISMIR.

[128]  Ching-Hua Chuan,et al.  A Dynamic Programming Approach to the Extraction of Phrase Boundaries from Tempo Variations in Expressive Performances , 2007, ISMIR.

[129]  Aaron Williamon,et al.  Time-Dependent Characteristics of Performance Evaluation , 2007 .

[130]  Masataka Goto Active Music Listening Interfaces Based on Signal Processing , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[131]  Esteban Maestre,et al.  Performance-Based Interpreter Identification in Saxophone Audio Recordings , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[132]  Craig Stuart Sapp Comparative Analysis of Multiple Musical Performances , 2007, ISMIR.

[133]  Christopher Raphael,et al.  A Simple Algorithm for Automatic Generation of Polyphonic Piano Fingerings , 2007, ISMIR.

[134]  David P. Helmbold,et al.  Modeling, analyzing, and synthesizing expressive piano performance with graphical models , 2006, Machine Learning.

[135]  Jie Liu,et al.  ESP: roadmaps as constructed interpretations and guides to expressive performance , 2006, AMCMM '06.

[136]  Gerhard Widmer,et al.  Relational IBL in classical music , 2006, Machine Learning.

[137]  Anders Friberg,et al.  pDM: An Expressive Sequencer with Real-Time Control of the KTH Music-Performance Rules , 2006, Computer Music Journal.

[138]  S. Dixon,et al.  PERCEPTUAL SMOOTHNESS OF TEMPO IN EXPRESSIVELY PERFORMED MUSIC , 2006 .

[139]  J. Sundberg,et al.  Overview of the KTH rule system for musical performance. , 2006 .

[140]  Marcus T. Pearce,et al.  The construction and evaluation of statistical models of melodic structure in music perception and composition , 2005 .

[141]  Efstathios Stamatatos,et al.  Automatic identification of music performers with learning ensembles , 2005, Artif. Intell..

[142]  Jie Liu,et al.  ESP: A Driving Interface for Expression Synthesis , 2005, NIME.

[143]  Anders Friberg,et al.  Home conducting - control the Overall Musical expression with gestures , 2005, ICMC.

[144]  Gerhard Widmer,et al.  The "Air Worm": an Interface for Real-Time manipulation of Expressive Music Performance , 2005, ICMC.

[145]  Henkjan Honing Timing is Tempo-Specific , 2005, ICMC.

[146]  John Shawe-Taylor,et al.  Using string kernels to identify famous performers from their playing style , 2004, Intell. Data Anal..

[147]  Henkjan Honing,et al.  Computational modeling of music cognition: a case study on model selection. , 2006 .

[148]  Giovanni De Poli Methodologies for Expressiveness Modelling of and for Music Performance , 2004 .

[149]  Gerhard Widmer,et al.  Computational Models of Expressive Music Performance: The State of the Art , 2004 .

[150]  Haruhiro Katayose,et al.  Rencon 2004: Turing Test for Musical Expression , 2004, NIME.

[151]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[152]  Manfred Clynes,et al.  Generative Principles of Musical Thought Integration of Microstructure with Structure. , 2004 .

[153]  Werner Goebl,et al.  Visualizing Expressive Performance in Tempo—Loudness Space , 2003, Computer Music Journal.

[154]  Gerhard Widmer,et al.  Playing Mozart by Analogy: Learning Multi-level Timing and Dynamics Strategies , 2003 .

[155]  John Rink In Respect of Performance: The View from Musicology , 2003 .

[156]  Patrik N. Juslin,et al.  Five Facets of Musical Expression: A Psychologist's Perspective on Music Performance , 2003 .

[157]  A. Gabrielsson Music Performance Research at the Millennium , 2003 .

[158]  Gerhard Widmer,et al.  Discovering simple rules in complex data: A meta-learning algorithm and some surprising musical discoveries , 2003, Artif. Intell..

[159]  Haruhiro Katayose,et al.  After the first year of Rencon , 2003, ICMC.

[160]  Peter Desain,et al.  Effects of Tempo on the Timing of Simple Musical Rhythms , 2002 .

[161]  H. Tekman Perceptual Integration of Timing and Intensity Variations in the Perception of Musical Accents , 2002, The Journal of general psychology.

[162]  John Rink Musical Performance: List of contributors , 2002 .

[163]  John Rink,et al.  Musical Performance: A Guide to Understanding , 2002 .

[164]  Haruhiro Katayose,et al.  RENCON: toward a new evaluation system for performance rendering systems , 2002, ICMC.

[165]  Christopher Raphael,et al.  Synthesizing Musical Accompaniments With Bayesian belief networks , 2001 .

[166]  S. Davies Philosophical perspectives on music's expressiveness , 2001 .

[167]  P. Juslin Communicating emotion in music performance: A review and a theoretical framework , 2001 .

[168]  Anders Friberg,et al.  Emotional Coloring of Computer-Controlled Music Performances , 2000, Computer Music Journal.

[169]  Johan Sundberg,et al.  Generating Musical Performances with Director Musices , 2000, Computer Music Journal.

[170]  Gerhard Widmer,et al.  Large-scale Induction of Expressive Performance Rules: First Quantitative Results , 2000, ICMC.

[171]  J. Sundberg,et al.  Does music performance allude to locomotion? A model of final ritardandi derived from measurements of stopping , 1999 .

[172]  A. Gabrielsson The Performance of Music , 1999 .

[173]  Roberto Bresin,et al.  Artificial neural networks based models for automatic performance of musical scores , 1998 .

[174]  B. Repp Obligatory “expectations” of expressive timing induced by perception of musical structure , 1998, Psychological research.

[175]  C. Palmer Music performance. , 1997, Annual review of psychology.

[176]  B. Repp The Art of Inaccuracy: Why Pianists' Errors Are Difficult to Hear , 1996 .

[177]  Gerhard Widmer,et al.  Learning expressive performance: The structure‐level approach , 1996 .

[178]  C. Palmer Anatomy of a Performance: Sources of Musical Expression , 1996 .

[179]  Emilios Cambouropoulos,et al.  Musical Rhythm: A Formal Model for Determining Local Boundaries, Accents and Metre in a Melodic Surface , 1996, Joint International Conference on Cognitive and Systematic Musicology.

[180]  Gerhard Widmer,et al.  Modeling the rational basis of musical expression , 1995 .

[181]  John Rink The Practice of Performance: STRUCTURE AND MEANING IN PERFORMANCE , 1995 .

[182]  Peter Desain,et al.  Does expressive timing in music performance scale proportionally with tempo? , 1994 .

[183]  Eric Clarke,et al.  Imitating and Evaluating Real and Transformed Musical Performances , 1993 .

[184]  Robert Rowe,et al.  Interactive Music Systems: Machine Listening and Composing , 1992 .

[185]  N. Todd The dynamics of dynamics: A model of musical expression , 1992 .

[186]  Eugene Narmour,et al.  The Analysis and Cognition of Basic Melodic Structures: The Implication-Realization Model , 1990 .

[187]  Roger A. Kendall,et al.  The Communication of Musical Expression , 1990 .

[188]  David Huron,et al.  The Avoidance of Inner-Voice Entries: Perceptual Evidence and Musical Practice , 1989 .

[189]  John A. Sloboda,et al.  The performance of music , 1986 .

[190]  H. C. Longuet-Higgins,et al.  The Rhythmic Interpretation of Monophonic Music , 1984 .

[191]  Roger B. Dannenberg,et al.  An On-Line Algorithm for Real-Time Accompaniment , 1984, ICMC.

[192]  R. Jackendoff,et al.  A Generative Theory of Tonal Music , 1985 .

[193]  Johan Sundberg,et al.  Musical Performance: A Synthesis-by-Rule Approach , 1983 .

[194]  H C Longuet-Higgins,et al.  The Perception of Musical Rhythms , 1982, Perception.

[195]  R. Rasch,et al.  The perceptual onset of musical tones , 1981, Perception & psychophysics.

[196]  J. Russell A circumplex model of affect. , 1980 .

[197]  Alf Gabrielsson,et al.  Performance of rhythm patterns , 1974 .

[198]  Manfred Clynes,et al.  Toward a Theory of Man: Precision of Essentic form in Living Communication , 1969 .

[199]  Hilla Peretz,et al.  Ju n 20 03 Schrödinger ’ s Cat : The rules of engagement , 2003 .

[200]  Robert O. Gjerdingen,et al.  The Psychology of Music , 1972 .

[201]  Alfred Binet,et al.  Recherches graphiques sur la musique , 1895 .