Genre Classification via an LZ78-Based String Kernel

We develop the notion of normalized information distance (NID) [7] into a kernel distance suitable for use with a Support Vector Machine classifier, and demonstrate its use for an audio genre classification task. Our classification scheme involves a relatively small number of low-level audio features, is efficient to compute, yet generates an accuracy which compares well with recent works.

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