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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.2

This paper discusses the current state of knowledge on musical pattern discovery. Various studies propose computational methods to find repeated musical patterns. Our detailed review of these studies reveals important challenges in musical pattern discovery research: different methods have not yet been directly compared, and the influence of music representation and filtering on the results has not been assessed. Moreover, we need a thorough understanding of musical patterns as perceived by human listeners. A sound evaluation methodology is still lacking. Consequently, we suggest perspectives for musical pattern discovery: future research can provide a comparison of different methods, and an assessment of different music representations and filtering criteria. A combination of quantitative and qualitative methods can overcome the lacking evaluation methodology. Musical patterns discovered by human listeners form a reference, but also an object of study, as computational methods can help us understand the criteria underlying human notions of musical repetition.

Much research has been devoted to the classification of folk songs, revealing that variants are recognised based on salient melodic segments, such as phrases and motifs, while other musical material in a melody might vary considerably. In order to judge similarity of melodies on the level of melodic segments, a successful similarity measure is needed which will allow finding occurrences of melodic segments in folk songs reliably. The present study compares several such similarity measures from different music research domains: correlation distance, city block distance, Euclidean distance, local alignment, wavelet transform and structure induction. We evaluate the measures against annotations of phrase occurrences in a corpus of Dutch folk songs, observing whether the measures detect annotated occurrences at the correct positions. Moreover, we investigate the influence of music representation on the success of the various measures, and analyse the robustness of the most successful measures over subsets of the data. Our results reveal that structure induction is a promising approach, but that local alignment and city block distance perform even better when applied to adjusted music representations. These three methods can be combined to find occurrences with increased precision.