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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
2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 2
AGENDA
INTRODUCTION INTERPRETATION PLANNING MOVEMENT
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
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
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
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
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
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
2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 9
AGENDA
INTRODUCTION INTERPRETATION PLANNING MOVEMENT
2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 10
System of Communication
Chain of events …
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
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
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
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
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
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
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
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
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
2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 20
AGENDA
INTRODUCTION INTERPRETATION PLANNING MOVEMENT
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
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
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
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
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
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
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
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
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
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
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
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
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
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
2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 35
AGENDA
INTRODUCTION INTERPRETATION PLANNING MOVEMENT
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
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
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
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
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
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)
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
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
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
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
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
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
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
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
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)
2006-01-11 ISE599 (ISE575b / CSCI575b / EE675b pending): Computational Modeling of Expressive Performance 51
AGENDA
INTRODUCTION INTERPRETATION PLANNING MOVEMENT
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
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
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
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
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