Identifying violin performers by their expressive trends
Intelligent Data Analysis - Machine Learning and Music
A state of the art on computational music performance
Expert Systems with Applications: An International Journal
Efficient evaluation of large sequence kernels
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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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.