C. Saunders, Gerhard Widmer, J. Shawe-Taylor,
"Using String Kernels to Identify Famous Performers from their Playing Style."
, in Intelligent Data Analysis, Vol. 12, Nummer 4, 2008
Original Titel:
Using String Kernels to Identify Famous Performers from their Playing Style.
Sprache des Titels:
Englisch
Original Kurzfassung:
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.
Forschungs-