We present an automatic genre classification system based on melodic features. First a ground truth genre dataset composed of polyphonic music excerpts is compiled. Predominant melodic pitch contours are then estimated, from which a series of descriptors is extracted. These features are related to melody pitch, variation and expressiveness (e.g. vibrato characteristics, pitch distributions, contour shape classes). We compare different standard classification algorithms to automatically classify genre using the extracted features. Finally, the model is evaluated and refined, and a working prototype is implemented. The results show that the set of melody descriptors developed is robust and reliable. They also reveal that complementing low level timbre features with high level melody features is a promising direction for genre classification. i iiAcknowledgements I would like to thank my supervisors, Emilia Gómez and Justin Salamon, for their invaluable help and support throughout this year. Xavier Serra for giving me the opportunity to participate in this master. Perfecto Herrera and Enric Guaus for th
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