Tuukka Tervo's presentation at the Colloquium on 29th October, on Sensory Dissonance Models

1. Sensory Dissonance
Models
Tuukka Tervo - Colloquium 29.10.2009

2. Sensory Dissonance
Models
There are two aspects of dissonance
perception
1. learned or top-down or contextual
2. innate or bottom-up or sensory

3. Sensory Dissonance
Models
Sensory dissonance is explained in terms of
• Physical properties of sound
• Physiological properties of the auditory
system

4. Sensory Dissonance
Models
Computer programs that…
…simulate the sensory process of
dissonance perception.
…give an estimate of the degree of
perceived dissonance of a given sound.

5. Sensory Dissonance
Models
Two types
• Auditory models
• Curve-mapping models

6. Sensory Dissonance
Models
Auditory models
• Based on models of the auditory periphery
• e.g. Leman (2000)

7. Sensory Dissonance
Models
Curve-mapping models
• Based on empirical data from Plomp and
Levelt (1965)
• e.g. Sethares (1999), Vassilakis (2001)

8. Curve-mapping Models
From Plomp & Levelt 1965
Sensory dissonance of a sine
tone pair as a function of
frequency difference on a critical
bandwidth scale

9. Sensory Dissonance
Models
Why try to model sensory dissonance
perception?
• To gain better understanding about its
contribution to the organisation of music.
• May be useful for MIR tasks.
• May be useful for studies of higher-level
processing of music, e.g. music-induced
emotions.

10. Research Question
Can the models of sensory dissonance predict
the perceived degree of dissonance of music?

11. Method
! Listening experiment to gather behavioural
data on dissonance perception.
! Simulating sensory dissonance processing using
various models.
! Statistical analysis of the relation between the
models' predictions and the behavioural data.

12. Participants
Two groups of 16
Students of musicology and music education
at the University of Jyväskylä

13. Stimuli
A. Piano music (Keith Jarrett)
50 x 5 seconds
B. Drone music (Jim O’Rourke, Phill Niblock)
50 x 5 seconds
C. Synthesized chords
20 x 3 seconds

14. Procedure
Each stimulus is rated on a scale from
1 (consonant) to 7 (dissonant).
Group 1
First stimuli A, then stimuli B or vice versa
Group 2
Stimuli A and B mixed, then stimuli C

15. Calculating Sensory
Dissonance
Models implemented in Matlab
Sethares' (1999) and Vassilakis' (2001)
curve-mapping models in the MIRtoolbox
at the Finnish Centre of Excellence in Interdisciplinary Music
Research, University of Jyväskylä
Leman's (2000) auditory model in the
IPEMtoolbox
at the Institute for Psychoacoustics and Electronic Music research
center of the Department of Musicology at the Ghent University

16. Calculating Sensory
Dissonance
5 seconds of audio

17. Calculating Sensory
Dissonance
50 ms frame

18. Calculating Sensory
Dissonance
Spectrum of the
50 ms frame

19. Calculating Sensory
Dissonance
Peak-picking

20. Calculating Sensory
Dissonance
Roughness of
each frame

21. Calculating Sensory
Dissonance
Mean roughness

22. Correlation between
predictions and ratings
Chords
r = 0.7663
p < 0.01

23. Correlation between
predictions and ratings
Drone
r = 0.5790
p < 0.01

24. Correlation between
predictions and ratings
Piano
r = 0.2708
p > 0.05

25. Some Conclusions
Curve-mapping models can predict the
perceived dissonance reasonably well for…
! …isolated chords.
! …drone music.
Difficulties with piano music. Why?
Non-sensory aspects affect the ratings?
Sharp attacks cause the models to detect
erratic dissonance peaks?

26. References
• Leman, M. 2000. Visualization and Calculation of the Roughness of
Acoustical Musical Signals Using the Synchronization Index Model
(SIM). Proceedings of the COST G-6 Conference on Digital Audio
Effects. Retrieved from: http://profs.sci.univr.it/~dafx/Final-Papers/
pdf/Leman_DAFXFinalPaper.pdf
• Plomp, R. & Levelt, W. J. M. 1965. Tonal Consonance and Critical
Bandwidth. Journal of the Acoustical Society of America, 38, 548-560.
• Sethares, W. 1999. Tuning, Timbre, Spectrum, Scale. Berlin,
Heidelberg, New York: Springer-Verlag.
• Vassilakis, P. N. 2001. Perceptual and Physical Properties of Amplitude
Fluctuation and their Musical Significance. Los Angeles: University of
California. Doctoral dissertation.