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Using String Kernels to Identify Performers from their Playing Style

Using String Kernels to Identify Performers from their Playing Style
Using String Kernels to Identify Performers from their Playing Style
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 works, 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 shows that this can deliver better performance than methods previously used in the literature when used in conjunction with a Support Vector Machine.
3-540-23105-6
Hardoon, David R.
05549e24-da95-4690-a3e2-3c672d2342b8
Saunders, Craig
26634635-4d4d-4469-b9ec-1d68788aa47a
Shawe-Taylor, John
b1931d97-fdd0-4bc1-89bc-ec01648e928b
Widmer, Gerhard
051165fa-ee91-44ee-b89a-4c7f6c0e056d
Hardoon, David R.
05549e24-da95-4690-a3e2-3c672d2342b8
Saunders, Craig
26634635-4d4d-4469-b9ec-1d68788aa47a
Shawe-Taylor, John
b1931d97-fdd0-4bc1-89bc-ec01648e928b
Widmer, Gerhard
051165fa-ee91-44ee-b89a-4c7f6c0e056d

Hardoon, David R., Saunders, Craig, Shawe-Taylor, John and Widmer, Gerhard (2004) Using String Kernels to Identify Performers from their Playing Style. European Conference on Machine Learning (ECML), Pisa, Italy. 19 - 23 Sep 2004.

Record type: Conference or Workshop Item (Paper)

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 works, 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 shows 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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More information

Published date: 2004
Additional Information: Event Dates: 20 - 24 September 2004
Venue - Dates: European Conference on Machine Learning (ECML), Pisa, Italy, 2004-09-19 - 2004-09-23
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 259591
URI: http://eprints.soton.ac.uk/id/eprint/259591
ISBN: 3-540-23105-6
PURE UUID: 3db42c54-5460-46c7-81b0-00b4d71ff66e

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Date deposited: 02 Mar 2005
Last modified: 23 Sep 2020 16:33

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