Sebastian Flossmann

The Magaloff Project: An Interim Report

Abstract 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.

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

Abstract For the past few decades there has been considerable scientific interest in expression in music performance (Gabrielsson, 2003). A particularly relevant aspect of music performance is expressive timing, that is, the intentional fluctuations of tempo during a performance. Accordingly, expressive timing has been one of the major topics in music performance research. As an expressive parameter, timing is used to clarify the musical structure of the piece (Clarke, 1988), among other things. The problem of explaining expressive timing in music performances can be regarded as a special case of a very wide range of problems where we want to learn experimentally about the temporal behaviour of some dynamical system based on limited observation. A common way of studying data in dynamical systems theory is by phase-plane representation. In this paper we argue that phase-plane representations of expressive timing provide a useful way of visualizing data, and furthermore, we show that such representations are promising in the context of performer characterization and identification.

Expressive Performance Rendering with Probabilistic Models

We present YQX, a probabilistic performance rendering system based on Bayesian network theory. It models dependencies between score and performance and predicts performance characteristics using information extracted from the score. We discuss the basic system that won the Rendering Contest RENCON 2008 and then present several extensions, two of which aim to incorporate the current performance context into the prediction, resulting in more stable and consistent predictions. Furthermore, we describe the first steps towards a multilevel prediction model: Segmentation of the work, decomposition of tempo trajectories, and combination of different prediction models form the basis for a hierarchical prediction system. The algorithms are evaluated and compared using two very large data sets of human piano performances: 13 complete Mozart sonatas and the complete works for solo piano by Chopin.