In this article we determine the role of different musical features for the human categorization of folk songs into tune families in a large collection of Dutch folk songs. Through an annotation study we investigate the relation between musical features, perceived similarity and human categorization in music. We introduce a newly developed annotation method which is used to create an annotation data set for 360 folk song melodies in 26 tune families. This dataset delivers valuable information on the contribution of musical features to the process of categorization which is based on assessing the similarity between melodies. The analysis of the annotation data set reveals that the importance of single musical features for assessing similarity varies both between and within tune families. In general, the recurrence of short characteristic motifs is most relevant for the perception of similarity between songs belonging to the same tune family. Global melodic features often used for the description of melodies (such as melodic contour) play a less important role. The annotation data set is a valuable resource for further research on melodic similarity and can be used as enriched “ground truth” to test various kinds of retrieval algorithms in Music Information Retrieval. Our annotation study exemplifies that assessing similarity is crucial for human categorization processes, which has been questioned within Cognitive Science in the context of rule-based approaches to categorization.
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