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A preliminary computational model of immanent accent salience in tonal music

A preliminary computational model of immanent accent salience in tonal music Richard Parncutt 1 , Erica Bisesi 1 , & Anders Friberg 2 1 University of Graz, Austria 2 KTH Stockholm, Sweden. SysMus. Research object (example). Chopin Prélude in A major p erformed by Claudio Arrau.

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A preliminary computational model of immanent accent salience in tonal music

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  1. A preliminary computational model of immanent accent salience in tonal music Richard Parncutt1, Erica Bisesi1, & Anders Friberg2 1University of Graz, Austria 2KTH Stockholm, Sweden SysMus

  2. Research object (example) Chopin Prélude in A major performed by Claudio Arrau Bisesi, Parncutt, Friberg

  3. Method: Performance renderingAim: Understand performance - not replace the performerApproach: Empirical quantitative science1. Develop a theory 2. Implement it as an algorithm3. Test its predictionsToo many variables!  Isolate them1. Separate composer (score) from performer 2. Consider only timing and dynamics (piano) kulturserver-nrw.de Bisesi, Parncutt, Friberg

  4. What motivates expressive piano performance? • Aim: What is the performer trying to achieve? • Means: On that basis, what do we expect? • 1. Aim: Participate in a cultural tradition • Means: Imitation of well-known performance patterns • 2. Aim: Speak to the audience • Means: Pseudo-random variation (speech without phonemes) • 3. Aim: Communicate gesturally with the audience • Means: Sound patterns based on physical gestures (kinematic) • 4. Aim: Communicate musical structure to the listener • Means: Emphasis of structurally important events Bisesi, Parncutt, Friberg

  5. Musical structure Global: form Intermediate: phrasing Local: accents A pianist can emphasize: The start or end of a new section The start or end of a phrase An important note or chord Tillmann, Bigand, and Madurell (1998) Bisesi, Parncutt, Friberg

  6. A taxonomy of accent Bisesi, Parncutt, Friberg

  7. A two-stage model of performance rendering • Analyse structure and estimate salience of immanent accents • Adjust timing and dynamics in the vicinity of accents Bisesi, Parncutt, Friberg

  8. 1. Immanent accents: Subjective salience estimatesStructurally important events in Chopin’s Prélude in A major Erica E. Bisesi Bisesi, Parncutt, Friberg

  9. 2. Performed accents at immanent accents: Subjective salience estimates Subjectiveevaluationofrecordedperformancesof 16 eminent pianists (meansandstandarddeviations)

  10. Models of timing and dynamics near accents Bisesi, Parncutt, Friberg

  11. Sample predictions to evaluatesubjectively or compare with recordings Bisesi, Parncutt, Friberg

  12. A preliminary computational model of immanent accent salience in tonal music • Grouping • Metrical • Melodic • Harmonic Bisesi, Parncutt, Friberg

  13. Grouping accent salience • Start and ends of phrases • Hierarchically structured • Procedure • Divide piece into 2 or 3 sections • Divide each section into 2 or 3 (etc.) • Follow composer’s markings • Estimate accent salience • Simple model: hierarchical depth • Complex : sum of salience at each level Bisesi, Parncutt, Friberg

  14. Metrical accent salience Bisesi, Parncutt, Friberg

  15. Melodic accent salience • Assumed to depend on: • distance from mean pitch • size of preceding leap • whether peak or valley Procedure Calculate (local) mean pitch Assign two values, S1 andS2, to each note S1 = |interval from mean in semitones| (if pitch is below mean, multiply S1 by 0.7) S2 = |preceding interval in semitones| (if interval is falling, multiply S2 by 0.7) Melodic salience = S1 * S2 Bisesi, Parncutt, Friberg

  16. Harmonic accent salience

  17. Calculated accent saliences Not including phrasing (grouping accents) Bisesi, Parncutt, Friberg

  18. Calculated accent saliences Not including phrasing (grouping accents) Bisesi, Parncutt, Friberg

  19. Next… • Computer interface • Representationof score withaccents • Pop-up boxesfortiming/dynamicfunctions • Psychological testing • Listenerratingsofartificialperformances • Stylisticissues • Performer styles • Intendedemotions • Shiftswithinandbetweenpieces • Combine withotherapproaches? • Cultural (arbitrarylearnedpatterns) • Aleatoric (speech-like) • Gestural (kinematic) Bisesi, Parncutt, Friberg

  20. A preliminary computational model of immanent accent salience in tonal music Richard Parncutt1, Erica Bisesi1, & Anders Friberg2 1University of Graz, Austria 2KTH Stockholm, Sweden SysMus • An approachtoperformancerenderingbased on • musicanalysis: accent • musicpsychology: communicationofstructure • 1. Analyse score for immanent accents • (grouping, metrical, melodic, harmonic) • 2. Estimatetheperceptualsalienceofeach • 3. Manipulatetiminganddynamicsneareach

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