Using string kernels to identify famous performers from their playing style

Article type: Research Article

Authors: Saunders, Craig^a | Hardoon, David R.^{b; *} | Shawe-Taylor, John^b | Widmer, Gerhard^c

Affiliations: [a] School of Electronics & Computer Science, ISIS Research Group, University of Southampton, Southampton, UK | [b] The Centre for Computational Statistics and Machine Learning, Department of Computer Science, University College London, London, UK | [c] Department of Medical Cybernetics and Artificial Intelligence, Medical University of Vienna, and Austrian Research Institute for Artificial Intelligence, Vienna, Austria

Correspondence: [*] Corresponding author. Tel.: +44 20 7679 0425; Fax: +44 20 7387 1397; E-mail: [email protected].

Abstract: In this paper we show a novel application of string kernels: that is to the problem of recognising famous pianists from their style of playing. The characteristics of performers playing the same piece are obtained from changes in beat-level tempo and beat-level loudness, which over the time of the piece form a performance worm. From such worms, general performance alphabets can be derived, and pianists' performances can then be represented as strings. We show that when using the string kernel on this data, both kernel partial least squares and Support Vector Machines outperform the current best results. Furthermore we suggest a new method of obtaining feature directions from the Kernel Partial Least Squares algorithm and show that this can deliver better performance than methods previously used in the literature when used in conjunction with a Support Vector Machine.

Keywords: String kernel, partial least squares, support vector machine, music

DOI: 10.3233/IDA-2008-12408

Journal: Intelligent Data Analysis, vol. 12, no. 4, pp. 425-440, 2008