2. 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

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

4. Method: Performance rendering Aim: Understand performance - not replace the performer Approach: Empirical quantitative science 1. Develop a theory 2. Implement it as an algorithm 3. Test its predictions Too many variables!  Isolate them 1. Separate composer (score) from performer 2. Consider only timing and dynamics (piano) Bisesi, Parncutt, Friberg kulturserver-nrw.de

5. 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 4 Bisesi, Parncutt, Friberg

6. 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 5 Bisesi, Parncutt, Friberg Tillmann, Bigand, and Madurell (1998)

7. A taxonomy of accent Bisesi, Parncutt, Friberg

8. A two-stage model of performance rendering 1.Analyse structure and estimate salience of immanent accents 2.Adjust timing and dynamics in the vicinity of accents 7 Bisesi, Parncutt, Friberg

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

10. 2. Performed accents at immanent accents: Subjective salience estimates 9 (means and standard deviations) Subjective evaluation of recorded performances of 16 eminent pianists

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

12. Sample predictions to evaluate subjectively or compare with recordings Bisesi, Parncutt, Friberg

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

14. Grouping accent salience 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 Start and ends of phrases Hierarchically structured Bisesi, Parncutt, Friberg

15. Metrical level Time signa- ture Level 0 Level 1 (beat) Level 2 Level 3 4/41/81/42/44/4 2/21/41/22/24/2 1/41/22/24/2 2/41/81/42/44/4 3/41/81/43/46/4 3/81/161/83/86/8 1/83/86/812/8 9/81/83/89/818/8 Metrical accent salience Bisesi, Parncutt, Friberg

16. 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 and S2, 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

17. Harmonic accent salience

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

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

20. Next… Computer interface Representation of score with accents Pop-up boxes for timing/dynamic functions Psychological testing Listener ratings of artificial performances Stylistic issues Performer styles Intended emotions Shifts within and between pieces Combine with other approaches? Cultural (arbitrary learned patterns) Aleatoric (speech-like) Gestural (kinematic) Bisesi, Parncutt, Friberg

21. 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 An approach to performance rendering based on music analysis: accent music psychology: communication of structure 1. Analyse score for immanent accents (grouping, metrical, melodic, harmonic) 2. Estimate the perceptual salience of each 3. Manipulate timing and dynamics near each