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Currents estimates put the canon of traditional Irish dance tunes at least 7,000 compositions. Given this diversity, a common problem faced by musicians and ethnomusicologists is identifying tunes from recordings. This is evident even in the number of commercial recordings whose title is gan ainm (without name). This work attempts to solve this problem by developing a Content Based Music Information Retrieval (CBMIR) System adapted to the characteristics of traditional Irish music. A system is presented called MATT2 (Machine Annotation of Traditional Tunes) whose primary goal is to annotate recordings of traditional Irish dance music with useful meta-data including tune names. MATT2 incorporates a number of novel algorithms for transcription of traditional music and for adapting melodic similarity measures to the creativity and style present in the playing of traditional music. It incorporates a new algorithm for filtering ornamentation notes and accommodating "the long note" in traditional music called Ornamentation Filtering using Adaptive Histograms (OFAH). A new algorithm is presented called TANSEY (Turn ANnotation from SEts using SimilaritY profiles) that annotates sets of tunes played segue as is the custom in traditional Irish dance music. The work presented is validated in experiments using 130 real-world field recordings of traditional music from sessions, classes, concerts and commercial recordings. Test audio includes solo and ensemble playing on a variety of instruments recorded in real-world settings such as noisy public sessions. Results are reported using standard measure from the field of information retrieval (IR) including accuracy, error, precision and recall and the system is compared to alternative approaches for CBMIR common in the literature.
Duggan, Brian and O'Shea, Brendan and Gainza, Mikel: The annotation of traditional Irish dance music using MATT2 and TANSEY. 8th. Annual Conference Technology and Telecommunication Conference, Galway Mayo Institute of Technology, Galway, Ireland, October, 2008.