This contribution gives an overview of the state of the art in the field of computational modeling of expressive music performance. The notion of predictive computational model is briefly discussed, and a number of quantitative models of various aspects of expressive performance are briefly reviewed. Four selected computational models are reviewed in some detail. Their basic principles and assumptions are explained and, wherever possible, empirical evaluations of the models on real performance data are reported. In addition to these models, which focus on general, common principles of performance, currently ongoing research on the formal characterisation of differences in individual performance style are briefly presented. 1.

This paper introduces a new method for an integrated display of tempo and loudness variations as measured in expressive music performance. This visualization technique includes data acquisition from both MIDI instruments and audio recordings, data reduction by smoothing measured performance data, and animated display on computer screen in synchrony with the music: A dot moves through a two-dimensional space of tempo (x axis) and loudness (y axis), leaving behind it a trajectory that may be interpreted as the intrinsic performance path of a particular performance. Snapshots of these trajectories can be used for detailed performance analyses. Expert performances of Chopin's E major Etude (op. 10, No. 3) and an algorithmic performance of Schubert's G flat major Impromptu (D. 899, No. 3) are compared with performances by famous pianists (Maurizio Pollini, Alfred Brendel). This method allows efficient display and analysis of large amounts of performance data and elucidates interactions between timing and dynamics

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The performance of music usually involves a great deal of interpretation by the musician. In classical music, final ritardandi are emblematic for the expressive aspect of music performance. In this paper we investigate to what degree individual performance style has an effect on the form of final ritardandi. To this end we look at interonset-interval deviations from a performance norm. We define a criterion for filtering out deviations that are likely to be due to measurement error. Using a machine-learning classifier, we evaluate an automatic pairwise pianist identification task as an initial assessment of the suitability of the filtered data for characterizing the individual playing style of pianists. The results indicate that in spite of an extremely reduced data representation, pianists can often be identified with accuracy significantly above baseline. 1. INTRODUCTION AND RELATED

The performance of music usually involves a great deal of interpretation by the musician. In classical music, the final ritardando is a good example of the expressive aspect of music performance. Even though expressive timing data is expected to have a strong component that is determined by the piece itself, in this paper we investigate to what degree individual performance style has an effect on the timing of final ritardandi. The particular approach taken here uses Friberg and Sundberg’s kinematic rubato model in order to characterize performed ritardandi. Using a machinelearning classifier, we carry out a pianist identification task to assess the suitability of the data for characterizing the individual playing style of pianists. The results indicate that in spite of an extremely reduced data representation, when cancelling the piece-specific aspects, pianists can often be identified with accuracy above baseline. This fact suggests the existence of a performer-specific style of playing ritardandi. 1.