Phase-plane Representation and Visualization of Gestural Structure in Expressive Timing

Grachten, Maarten; Goebl, Werner; Flossmann, Sebastian; Widmer, Gerhard

doi:10.1080/09298210903171160

Cited by 9 publications

(8 citation statements)

References 18 publications

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“…In a strict sense, the representation is redundant, because the derivatives (the second dimension of the plane) are fully determined by the tempo curve (the first dimension). Still, as one can demonstrate with qualitative examples (Grachten et al 2008), the representation has certain advantages when used for visual analysis. It permits the human analyst to perceive expressive “gestures” and “gestalts,” and variations of these, much more easily and directly than in plain tempo curves.…”

Section: Studying a Creative Behavior With Ai Methodsmentioning

confidence: 99%

“…The sidebar Visualizing Expressive Timing in the Phase Plane gives some basic information about the computation and meaning of smoothed timing trajectories in the phase plane. A more detailed description of the computation process and the underlying assumptions can be found in Grachten et al (2009). We will also encounter phase-plane plots later in this article, when we compare the timing behavior of famous pianists.…”

Section: Visualizing Expressive Timing In the Phase Planementioning

confidence: 99%

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YQX Plays Chopin

2009

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The article is about AI research in the context of a complex artistic behavior: expressive music performance. A computer program is presented that learns to play piano with 'expression' and that even won an international computer piano performance contest. A superficial analysis of an expressive performance generated by the system seems to suggest creative musical abilities. After a critical discussion of the processes underlying this behavior, we abandon the question of whether the system is really creative, and turn to the true motivation that drives this research: to use AI methods to investigate and better understand music performance as a human creative behavior. A number of recent and current results from our research are briefly presented that indicate that machines can give us interesting insights into such a complex creative behavior, even if they may not be creative themselves.

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Section: Studying a Creative Behavior With Ai Methodsmentioning

confidence: 99%

Section: Visualizing Expressive Timing In the Phase Planementioning

confidence: 99%

YQX Plays Chopin

2009

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“…Derivatives can easily be computed due to the piece-wise polynomial form. More details can be found in Grachten et al (2009). The method for computing phase-planes used here differs slightly from the one presented in Grachten et al (2009).…”

Section: Phase-plane Representations For Visual Analysis Of Timingmentioning

confidence: 98%

“…More details can be found in Grachten et al (2009). The method for computing phase-planes used here differs slightly from the one presented in Grachten et al (2009). Rather than approximating tempo data, which are derived from measured onset times in the performance, we fit the measurements directly as a scoreperformance time-map.…”

Section: Phase-plane Representations For Visual Analysis Of Timingmentioning

confidence: 99%

“…The phase-plane representations is a visualisation tool common in physics and dynamic systems theory. It was introduced in the context of music performance research (Grachten et al, 2008;Ramsay, 2003;Grachten et al, 2009), mainly because of its emphasis on dynamic aspects of the data. This is of particular relevance for the analysis of "expressive gestures", which are (at least partially) manifest as fluctuations in timing and loudness of performed music.…”

Section: Phase-plane Representations For Visual Analysis Of Timingmentioning

confidence: 99%

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The Magaloff Project: An Interim Report

Flossmann

Goebl

Grachten

et al. 2010

Journal of New Music Research

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One of the main difficulties in studying expression in musical performance is the acquisition of data. While audio recordings abound, automatically extracting precise information related to timing, dynamics, and articulation is still not possible at the level of precision required for large-scale music performance studies. In 1989, the Russian pianist Nikita Magaloff performed essentially the entire works for solo piano by Frédéric Chopin on a Bösendorfer SE, a computer-controlled grand piano that precisely measures every key and pedal action by the performer. In this paper, we describe the process and the tools for the preparation of this collection, which comprises hundreds of thousands of notes. We then move on to presenting the results of initial exploratory studies of the expressive content of the data, specifically effects of performer age, performance errors, between-hand asynchronies, and tempo rubato. We also report preliminary results of a systematic study of the shaping of particular rhythmic passages, using the notion of phase-plane trajectories. Finally, we briefly describe how the Magaloff data were used to train a performance rendering system that won the 2008 Rencon International Performance Rendering Contest.

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