Global Feature Versus Event Models for Folk Song Classification
نویسندگان
چکیده
Music classification has been widely investigated in the past few years using a variety of machine learning approaches. In this study, a corpus of 3367 folk songs, divided into six geographic regions, has been created and is used to evaluate two popular yet contrasting methods for symbolic melody classification. For the task of folk song classification, a global feature approach, which summarizes a melody as a feature vector, is outperformed by an event model of abstract event features. The best accuracy obtained on the folk song corpus was achieved with an ensemble of event models. These results indicate that the event model should be the default model of choice for folk song classification.
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