Music genre classification with Self-Organizing Maps and edit distance

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We propose a method for music genre classification based on a Self-Organizing Map (SOM) - type network. Music pieces are viewed as sequences of pitch and timbre signals. We define a similarity measure between these sequences, derived from the Levenshtein (edit) distance. In contrast to the standard Levenshtein distance, our similarity measure is able to operate on a continuous vector space. Using this measure, we map the input music pieces on a SOM. The SOM is trained using a special string adjustment mechanism, which is determined by an algebraic equation. Our method turns out to achieve better classification accuracy than some other recent techniques. The feature set identified by SOM provides superior classifier accuracy compared to the same classifier applied on a random feature set of the same size. On standard benchmarks, two of our derived classifiers achieve accuracies of 97.32% (using a slow kNN learning algorithm), respectively 95.20% (using a SOM - type algorithm).