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Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology of Performance McGill University, Montreal, Quebec, Canada Presented by Elaine Chew On January 11, 2006, as part of ISE 599: Topics in Engineering Approaches to Music Cognition - Computational Models of Expressive Performance

Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

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Page 1: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

Review of MUSIC PERFORMANCE

by Caroline PalmerAnn. Rev. Psychol. 1997, 48:115-38

Professor, Dept of PsychologyCanada Research ChairCognitive Neuropsychology of PerformanceMcGill University, Montreal, Quebec, Canada

Presented by Elaine ChewOn January 11, 2006, as part of ISE 599: Topics in Engineering Approaches to Music Cognition- Computational Models of Expressive Performance

Page 2: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 2

AGENDA

INTRODUCTION INTERPRETATION PLANNING MOVEMENT

Page 3: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 3

Forms of Performance

Sight-reading Performing well-learned music from

memory or notation Improvising Playing by ear

Page 4: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 4

Serial Order and Timing Issues

Skilled serial action: • speaking, typing, performing music

Activity must be centrally linked• Little time for feedback for planning• Can be performed w/o kinesthetic feedback

Accurate temporal control: rhythm• Basis for dev models of timing mechanisms• Consensus on requirements for accuracy

Page 5: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 5

Purpose of psychological studies

Develop theories of performance mechanisms (cognitive/motor constraints)

Explain treatment of structural ambiguities Understand relationship between

performance and perception

Page 6: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 6

Components of performance

Interprete piece conceptually Retrieve musical structures and units

from memory Prepare for production Transformed into appropriate

movements

Page 7: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 7

Methodological Issues

Wealth of data• Separating signal from noise• Focus on movement-based information

Judgement of representative piece• Recognized level of performer expertise• Large samples of data hard to find• Rely on converging evidence from both

small and large sample studies

Page 8: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 8

Performance Expression

Variations in timing, intensity (dynamics), timbre, and pitch • form the microstructure of a performance• differentiate it from another of the same pc

Measurements• deviation from fixed or regular values as

notated in score• Relative to performance itself, e.g. pattern of

deviation with repect to a unit s.a. a phrase

Page 9: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 9

AGENDA

INTRODUCTION INTERPRETATION PLANNING MOVEMENT

Page 10: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 10

System of Communication

Chain of events …

Page 11: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 11

System of Communication

Composers code musical ideas in notation

Performers recode from notation to acoustical signal• Includes performer’s conceptual

interpretation of composition

Listeners recode from acoustical signal to ideas

Page 12: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 12

Interpretation

Performer’s individualistic modeling of a piece according to their own ideas or musical intentions

In western music notation:• Pitch and duration (clear)• Intensity and tone quality (approx)• Group boundaries, metrical levels higher than the

bar, patterns of motion, tension, and relaxation (unspec, implicit)

Could explain inter- and intra-performer performances of the same piece

Page 13: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 13

Role of Analysis

Every performance involves some kind of interpretation or analysis

Analysis offers explanations for the content of a composition as a• Hierarchy of whole/part relations• Linear course following harmonic tension• Series of moods that result in unity of character

Analysis does not indicate how a performer actually produces a desired interpretation

Page 14: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 14

Goal of Interpretation

Convey the meaning of the music• Structure, emotion, and physical movement

Highlight particular structural content Highlight particular emotional content

Page 15: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 15

Highlighting structural content

Nakamura (1987): • Compared musicians’ performances of baroque sonata with

notated interpretations of dynamics• Perceived dynamics matched intended fairly well, even when

underlying acoustic changes were not identifiable Palmer (1989):

• Compared pianists’ notated intepretations of phrase structure and melody with expressive timing patterns

• Melody lead and slowing of tempo at phrase boundaries observed• Expressive timing patterns decr when attempting to play w/o

interpretation, incr in exaggerated interp Palmer (1988):

• Expressive timing patterns incr from novices to experts, during practice of unfamiliar piece, changed in diff interp by same perf

Page 16: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 16

Implication of structural content interp

Palmer (1992):• Pitch deletions tend to occur within

phrases, and pitches tend to persevere at phrase boundaries

• Interpretations strengthen phrase boundaries relative to other locations

Palmer & van de Sande (1993, 1995):• Melodic events are correctly retrieved and

produced relative to nonmelodic events

Page 17: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 17

Goal of Interpretation

Convey the meaning of the music• Structure, emotion, and physical movement

Highlight particular structural content Highlight particular emotional content

Page 18: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 18

Highlighting emotional content

Langner (1953): Music shd sound the way moods feel Gabrielsson (1995), G. & Juslin (1996): • Compared performers’ interp of emotional content with

their use of expression• Happy/angry - faster, larger dynamic range• Soft/sad - slower, smaller dynamic range

Ashkenfelt (1986):• Similar results in tender/aggressive experiments

Schmalfeldt (1985), Shaffer (1995):• Emotional content as part of narrative, dramatic char,

thematic content, conceptions of large-scale structures

Page 19: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 19

Role of experience

Musical experience enhances ability to use and identify interpretations

Nonmusicians can pick up interpretative aspects of performance• Discern general differences among mechanical,

expressive, exaggerated perf• Can hear intended phrase structure• Cannot always find melody interpretation

Sufficiency of expressive features to convey intepretations

Page 20: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 20

AGENDA

INTRODUCTION INTERPRETATION PLANNING MOVEMENT

Page 21: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 21

Planning and Memory

Related to melodic, harmonic and diatonic structures• Chord errors occur more in homophonic mus• Single note errors more in polyphonic music• Mistakes originate more from key of piece• Mistakes tend to be of same chord type• Child singing pitch errors tend to be harmonically

related to intended events• Pianists’ sight-reading errors in pcs with deliberate

pitch alterations indicate tacit melodic/harmonic knowledge

Page 22: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 22

Subsequence Partitioning

Partitioning into phrases• Errors originate more from same phrase• Interacting errors rarely crossed phrase

boundaries (like in speech)• Errors increased when melodic, metrical,

rhythmic accents unaligned Planning ahead• Eye-hand span 7-8 events, or to phrase end• Range of planning affected by serial & struct

Page 23: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 23

Syntax of Musical Structure

Events at most salient levels are commonly emphasized in performance• Tactus: foot tapping metric level• Phrase: partitioning of melody

More important events are processed at deeper hierarchical (structural) levels• Improvisations tend to retain only

structurally important events from abstract hierarchical levels of reduction

Page 24: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 24

Structure-Expression Link: Phrases

Decrease of tempo/dynamics at end of phrases Amt of slowing at a boundary reflects depth of

phrase embedding More important segments have greater phrase-

final lengthening Greatest corr bet expr timing and intensity

found at interm phrase level Performers’ notated/sounded interpretations

differ most at levels lower than phrase

Page 25: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 25

Structure-Expression Link: Meter

Events on strong beats often lengthened, have delayed onsets

Events on metrical accents louder, longer, more legato

Listeners’ judgements of metric interpretation aligned best with experienced pianists’ intended meter

Articulation most often used as metric cue, loudness not always present

No one set of necessary and sufficient expressive cues to denote meter exist

Page 26: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 26

Structure-Expression Link: Rhythm

Systematic deviations in Vienese Waltz:• Short 1 - long 2 - 3

Page 27: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 27

Expressive Timing Patterns

Structure• Meter, accent pattern, simplicity (dur ratios)

Motion• Rapidity, tempo, forward movement

Emotion• Vitality, excitedness, playfulness

Page 28: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 28

Comments

Melodic/metrical accents sometimes altered by presence of rhythmic accents or each other

Melody lead may serve to separate voices perceptually

Page 29: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 29

Generative model of expr synthesis

Clarke (1993, 1995):• Systematic patterns of expression result from

transformations of the performer’s internal representation of musical structure

Support for view: the abilities to• Replicate same expressive timing profile with little

variation across performances• Change interpretation and produce different

expression with little practice• Sight-read with appropriate expression

Page 30: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 30

Rule-based models

Sundberg et al (1983ab):• Differentiation, grouping, ensemble rules affect

event durations, intensities, pitch tunings, and vibrato

Clynes (1977,1983,1986):• Composer-specific inner pulses applied to different

levels of musical structure

Piece-specific factors contribute as much as piece-transcendent factors captured by rules

Page 31: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 31

Arguments against generative models

Performers can imitate expressive timing patterns with arbitrary relationships to musical structure

Accuracy worse with more disruptive structure-expression relationship, improved with repeated attempts

Suggests expression not generated solely from structural relationships

Page 32: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 32

Perceptual Functions

Communicate particular interpretations and resolve structural ambiguities

Compensate for perceptual constraints of auditory system

Page 33: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 33

Other explanations for expression

Compensatory explanation:• Some notes played louder/longer because they would

be heard softer/shorter otherwise

Musical structure elicits expectations:• Detection of lengthening more difficult where expected• Detection accuracy inversely related to performer’s

natural use of lengthening in same piece

Structure constrains both perception and performance

Page 34: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 34

Music Theories

Narmour (1990,1996):• Model of melodic expectancy

Lerdahl (1996):• Model predicting tonal tension and relaxation

Listeners can apprehend predicted structures Expressive cues emphasize computed structures Interpretations constrained by composition

Page 35: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 35

AGENDA

INTRODUCTION INTERPRETATION PLANNING MOVEMENT

Page 36: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 36

Movement

Musical rhythm often defined relative to body movement

Different views on relationship:• Motor control - movement generating timing• Timekeeper - internal clock for anticipation

and coordination of gestures

Page 37: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 37

Timekeeper models

Role: regulate and coordinate complex time series, such as those produced between hands or between performers

Constructs beats at abstract level, providing temporal reference for future movements

Evidence: rhythm reproduction better for integer duration ratios

Page 38: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 38

Internal clocks

Single clock model Multiple timekeepers (Jones 1990 review) Attributed to perceptual encoding Attributed to production mechanisms

Page 39: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 39

Clock operation level

Tactus: most salient metrical level Preferred tactus ~ 600ms (spontaneous

clapping period) Typical inter-step interval ~ 540ms Listeners use motion to describe rhythmic

patterns when interbeat intervals ~ 650ms Time periods derived are multiples or

fractions of beat periods

Page 40: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 40

Source of temporal variance

Early models: partitioned temporal variance to lack of precision of timekeeper vs. motor response delay

Extended to hierarchical organizations of timekeepers at multiple metrical levels• Performed durations at metrical level less

variable than durations of residual nested events within that level

Page 41: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 41

Hierarchical clocks

Timekeeping most directly controled at intermediate metrical levels of the sub-beat, the beat, or the bar

Solo piano music: timekeeping controlled at the beat level (hands have independence in coordinating events below beat level)

Separate timekeepers controled timing of individual hands

Duet piano performance: highest precision (least variance) at bar level

(above studies assumed constant global tempo)

Page 42: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 42

Performance timing stability

Not only at tactus / beat / bar level Exists at level of entire piece. Durations of

string quartets over repeat performances highly consistent. • Std dev of piece duration ~1%• Less than variations in movement lengths

Proportional tempos theory: tempos of successive sections of music form simple integer ratios

Phase synchrony, esp at structural boundaries May reflect performer’s memory for tempo

Page 43: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 43

Movement

Musical rhythm often defined relative to body movement

Different views on relationship:• Motor control - movement generating timing• Timekeeper - internal clock for anticipation

and coordination of gestures

Page 44: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 44

Motor Programs

Contains representations of internal actions and processes that translate them into movement sequence

Accounts of motor equivalence across contexts

Possible proof: Relational invariance - tempo changes as parameter change

Page 45: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 45

Relational invariance

Relative durations of notes tend to vary across performances played at different tempi

Hypothesis: structural interpretation does not remain constant across performance tempo• # group boundaries incr at slower tempo

Practicing at different rate than intended performance might be counterproductive

Lesson: do not draw conclusions from average of performances over diff tempi

Page 46: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 46

Tempo changes perceived structure?

Tempo affects perception of duration patterns• Different perceptions may result for same relative

expressive timing pattern at different tempo

Repp (1995b):• Manipulated degree of expressive timing and

global tempo• Listeners preferred reduced expression with fast

tempo and augmented expression w slow

Page 47: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 47

Kinematic models

View: music performance and perception have origins in kinematic/dynamic characteristics of typical motor actions

E.g. walking -> beat Aesthetically satisfying performances

should satisfy kinematic constraints of biological motion

Page 48: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 48

Kinematic models

Final ritards modeled as variable curve followed be linear decrease in tempo

Feldman et al (1992): cubic polynomial models used to minimize jerk/jumpiness in connecting points of tempo changes

Repp (1992b): used quadratic

Page 49: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 49

Models with dynamics

Studies suggest coupling bet expr timing and dynamics

Todd (1992): proposed model where intensity proportional to square of vel. Used constant acceleration

Todd (1995): proposed auditory model of rhythm performance and perception• Temporal segmentation of onsets• Periodicity analysis• Sensory-motor feedback: tactus, body sway

Page 50: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 50

Arguments agains kinematic models

Physical notions of energy cannot be equated with psychological concepts of musical energy

Tempo changes guided by perceptual rather than kinematic properties:• Large tempo changes cannot occur too

quickly (perception to rhythmic categories)

Page 51: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 51

AGENDA

INTRODUCTION INTERPRETATION PLANNING MOVEMENT

Page 52: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 52

MUSIC PERFORMANCE

Empirical research review Sequence planning research review Motor control Perceptual consequences

Page 53: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 53

MUSIC PERFORMANCE

Empirical research review• Conceptual interpretations• Retrieval from memory of musical structures• Transformation into motor actions

Sequence planning research review Motor control Perceptual consequences

Page 54: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 54

MUSIC PERFORMANCE

Empirical research review Sequence planning research review• Hierarchical and associative retrieval influences• Style-specific syntactic influences• Constraints on range of planning

Motor control Perceptual consequences

Page 55: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 55

MUSIC PERFORMANCE

Empirical research review Sequence planning research review Motor control• Internal timekeeper models• Motor programs• Kinematic models

Perceptual consequences

Page 56: Review of MUSIC PERFORMANCE by Caroline Palmer Ann. Rev. Psychol. 1997, 48:115-38 Professor, Dept of Psychology Canada Research Chair Cognitive Neuropsychology

2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 56

MUSIC PERFORMANCE

Empirical research review Sequence planning research review Motor control Perceptual consequences• Successful communication of interpretations• Resolution of structural ambiguities• Concordance with listeners’ expectations