Abstract
Expressive performance is an indispensable part of music making. When playing a piece, expert performers shape various parameters (tempo, timing, dynamics, intonation, articulation, etc.) in ways that are not prescribed by the notated score, in this way producing an expressive rendition that brings out dramatic, affective, and emotional qualities that may engage and affect the listeners. Given the central importance of this skill for many kinds of music, expressive performance has become an important research topic for disciplines like musicology, music psychology, etc. This paper focuses on a specific thread of research: work on computational music performance models. Computational models are attempts at codifying hypotheses about expressive performance in terms of mathematical formulas or computer programs, so that they can be evaluated in systematic and quantitative ways. Such models can serve at least two main purposes: they permit us to systematically study certain hypotheses regarding performance; and they can be used as tools to generate automated or semi-automated performances, in artistic or educational contexts. The present article presents an up-to-date overview of the state of the art in this domain. We explore recent trends in the field, such as a strong focus on data-driven (machine learning); a growing interest in interactive expressive systems, such as conductor simulators and automatic accompaniment systems; and an increased interest in exploring cognitively plausible features and models. We provide an in-depth discussion of several important design choices in such computer models, and discuss a crucial (and still largely unsolved) problem that is hindering systematic progress: the question of how to evaluate such models in scientifically and musically meaningful ways. From all this, we finally derive some research directions that should be pursued with priority, in order to advance the field and our understanding of expressive music performance.
Highlights
The way a piece of music is performed is a very important factor influencing our enjoyment of music
We will refer to a model as static if its predictions only depend on a single event in time, and dynamic if its predictions can account for time-dependent changes
Examples of such approaches include the work by Kosta et al (2014, 2015, 2016), who focus on the relationship between dynamics markings and expressive dynamics, and the Basis Function Model (Grachten and Widmer, 2012)—a framework that encodes score properties via so-called basis functions— which attempts to quantify the contribution of a variety of score descriptors to expressive dynamics (Cancino-Chacón C.E. et al, 2017) and timing (Grachten and Cancino-Chacón, 2017)
Summary
The way a piece of music is performed is a very important factor influencing our enjoyment of music. The present article focuses on a narrower and more specific topic: computational models of expressive performance, that is, attempts at codifying hypotheses about expressive performance—as mappings from score to actual performance— in such a precise way that they can be implemented as computer programs and evaluated in systematic and quantitative ways This has developed into a veritable research field of its own over the past two decades, and the present work is not the first survey of its kind; previous reviews of computational performance modeling have been presented by De Poli (2004), Widmer and Goebl (2004), and Kirke and Miranda (2013).
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