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Using String Kernels to Identify Famous Performers from their Playing Style

Saunders, Craig, Hardoon, David, Shawe-Taylor, John and Widmer, Gerhard (2008) Using String Kernels to Identify Famous Performers from their Playing Style Intelligent Data Analysis, 12, (4)

Record type: Article


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.

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Published date: 2008
Additional Information: To appear
Organisations: Electronics & Computer Science


Local EPrints ID: 265198
ISSN: 1088-467x
PURE UUID: b05f7091-b7a9-4d7d-9358-0b1df402c23d

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Date deposited: 23 Feb 2008 16:00
Last modified: 18 Jul 2017 07:28

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Author: Craig Saunders
Author: David Hardoon
Author: John Shawe-Taylor
Author: Gerhard Widmer

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