Melodic Grouping in Music Information Retrieval: New Methods and Applications

Pearce, Marcus T.; Müllensiefen, Daniel and Wiggins, Geraint. 2010. Melodic Grouping in Music Information Retrieval: New Methods and Applications. In: Zbigniew W. Ras and Alicja Wieczorkowska, eds. Advances in Music Information Retrieval Advances in Music Information Retrieval. 274 Berlin and New York: Springer, pp. 365-390. ISBN 978-3-642-11673-5 [Book Section]

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Abstract or Description

We introduce the MIR task of segmenting melodies into phrases, summarise the musicological and psychological background to the task and review existing computational methods before presenting a new model, IDyOM, for melodic segmentation based on statistical learning and information-dynamic analysis. The performance of the model is compared to several existing algorithms in predicting the annotated phrase boundaries in a large corpus of folk music. The results indicate that four algorithms produce acceptable results: one of these is the IDyOM model which performs much better than naive statistical models and approaches the performance of the best-performing rule-based models. Further slight performance improvement can be obtained by combining the output of the four algorithms in a hybrid model, although the performance of this model is moderate at best, leaving a great deal of room for improvement on this task.

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Date Deposited:

28 Mar 2011 10:42

Last Modified:

29 Apr 2020 15:30


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