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Using String Kernels to Identify Famous Performers from their Playing Style

机译:使用字符串内核从他们的游戏风格中识别出名人

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摘要

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.
机译:在本文中,我们展示了弦乐内核的一种新型应用:即解决著名钢琴家演奏风格方面的问题。演奏同一乐段的演奏者的特征是从拍子水平速度和拍子水平响度的变化中获得的,这些变化随着演奏时间的推移而形成一种表演蠕虫。从这种蠕虫中,可以得出一般的演奏字母,然后将钢琴演奏者的演奏表示为字符串。我们表明,在此数据上使用字符串内核时,内核偏最小二乘和支持向量机均优于当前的最佳结果。此外,我们建议一种从核偏最小二乘算法中获取特征方向的新方法,并表明与支持向量机结合使用时,该方法可以提供比文献中先前使用的方法更好的性能。

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