Comparative Pattern Analysis of Cretan Folk Songs ∗ Darrell Conklin Department of Computer Science and Artificial Intelligence Universidad del Pa´ısVasco San Sebasti´an,Spain IKERBASQUE: Basque Foundation for Science Bilbao, Spain
[email protected] Christina Anagnostopoulou Department of Music Studies University of Athens Athens, Greece
[email protected] Abstract This paper reports on data mining of Cretan folk songs for distinctive patterns. A pattern is distinctive if it occurs with higher probability in a corpus as compared to an anticorpus. A small set of Cretan folk songs was collected, organized using a small knowledge base of classes, and mined using distinctive pattern discovery methods. Several highly distinctive and confident patterns emerge. keywords: ethnomusicology, data mining, folk song analysis, pattern discovery, classifi- cation 1 Introduction In recent years there is a renewed interest in folk song analysis partly driven by increasing interests in cultural heritage, and also by advances in music informatics methods. The ∗This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. Please cite the definitive version which was published at MML' 10, 3rd International Workshop on Machine Learning and Music, October 25, 2010, Florence, Italy, pages 33{36. 1 ability to make predictions, from music content, of song properties such as region, dance type, tune family, instrumentation, modality, and social function is an important part of the management of large corpora. Music data mining methods play a key role in building predictive models for folk song classification. The folk music of Crete, like all traditional Greek music, can be divided primarily into dance and non-dance (which are further subdivided into \Tavla" or \songs of the table" and \road" songs).