[published as: Desain, P and Honing, H. (1997) Structural Expression Component Theory (SECT), and a method for decomposing expression in music performance. in Proceedings of the Society for Music Perception and Cognition Conference., 38. Cambridge: MIT.]

Structural Expression Component Theory (SECT),

and a Method for Decomposing Expression in Music Performance

Peter Desain (1) & Henkjan Honing (1,2)

(1) NICI, University of Nijmegen

P.O. Box 9104, NL - 6500 HE Nijmegen, The Netherlands

desain@nici.kun.nl, honing@nici.kun.nl

(2) University of Amsterdam, Music Department

Spuistraat 134, NL - 1012 VB Amsterdam, The Netherlands

Although many generative models of expressive timing and dynamics (e.g., Clynes, 1987; Todd, 1989; Sundberg et al., 1989) exist that explain musical expression based on one kind of musical structure (e.g., meter, phrase, surface), an overall theory is lacking that describes how these different components are combined in performance. It is hard to find ways in which timing information of different nature can be combined, but a representation that keeps tempo-changes and time-shifts separate is a promising candidate. We will present such an approach, named structural expression component theory (SECT). The theory makes use of generalizations of existing generative models and extends them to deliver representations of musical timing that can be composed into the overall expression profiles.

The power of such an approach is that it makes it possible to fit the combined output of the generalized generative models directly to empirical data (i.e. actual musical performances), without one kind of musical expression confounding the validation of expression models based on another kind of structure. This method (implemented as a computer program called DISSECT in the POCO environment), next to obtaining the appropriate parameter setting for the individual models, also effectively separates the expressive signal into its components, each explained by one generative model. This enables us to determine the explanatory power of each model, and to estimate the amounts of variation that can be attributed to different musical structural descriptions in different musical styles and for various interpretations.

In cases where the method works well, and much of the expressive variation is explained, the decomposition of expression yields a good and economical encoding. For example, instead of a large set of individual tempo measurements per note, the memory load is only one set of parameters for a systematic metric tempo profile, plus a set of parameters for phrase-linked rubato, and so on. This economy of encoding can be interpreted as evidence for a corresponding mental representation of that specific structural aspect, and alternative mental representations can be investigated directly by redoing the decomposition analysis using different structural descriptions. This extends the technique from attributing expression to different known structural sources, towards the direct inference of mental representations from the expressive signal.

References