A Musical Robot that Synchronizes with a Coplayer Using Non-Verbal Cues

Lim, Angelica; Mizumoto, Takeshi; Ogata, Tetsuya; Okuno, Hiroshi G.

doi:10.1163/156855311x614626

Cited by 11 publications

(1 citation statement)

References 31 publications

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“…The ACCompanion (Cancino-Chacón et al, 2017a) is a system that combines an HMM-based monophonic score follower with a variant of the Basis Mixer to predict expressive timing, dynamics, and articulation for the accompaniment. Another interesting recent development in accompaniment systems is embodied human-computer interactions through humanoid robots (Hoffman and Weinberg, 2011;Lim et al, 2012;Solis and Takanishi, 2013;Xia, 2016). These robots could be used for studying the way humans interact with each other.…”

Section: Accompaniment Systemsmentioning

confidence: 99%

Computational Models of Expressive Music Performance: A Comprehensive and Critical Review

Cancino-Chacón

Grachten

Goebl

et al. 2018

Front. Digit. Humanit.

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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 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) approaches; 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.

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Section: Accompaniment Systemsmentioning

confidence: 99%