Abstract
This paper reports on a comparative study of computational
melody segmentation models based on repetition detection. For the comparison we implemented five repetition-based segmentation models, and subsequently evaluated their capacity to automatically find melodic phrase boundaries in a corpus of 200 folk melodies. We systematically investigate the effects that the choice of melodic representation, similarity measure, and parameter settings have on each model’s performances. We discuss at length issues such as parameter sensitivity, generalization capability, and efficiency. The best performing model employs a similarity matrix to identify repetitions, and selects which repetitions are used to segment the input melody using an optimisation-based search algorithm.
melody segmentation models based on repetition detection. For the comparison we implemented five repetition-based segmentation models, and subsequently evaluated their capacity to automatically find melodic phrase boundaries in a corpus of 200 folk melodies. We systematically investigate the effects that the choice of melodic representation, similarity measure, and parameter settings have on each model’s performances. We discuss at length issues such as parameter sensitivity, generalization capability, and efficiency. The best performing model employs a similarity matrix to identify repetitions, and selects which repetitions are used to segment the input melody using an optimisation-based search algorithm.
Original language | English |
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Title of host publication | Proceedings of the 9th Conference on Interdisciplinary Musicology (CIM14) |
Place of Publication | Berlin |
Publisher | SIMPK and ICCMR |
Pages | 143-148 |
Number of pages | 6 |
Publication status | Published - 2014 |
Event | Conference on Interdisciplinary Musicology - Berlin, Germany Duration: 4 Dec 2014 → 6 Dec 2014 |
Conference
Conference | Conference on Interdisciplinary Musicology |
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Country/Territory | Germany |
City | Berlin |
Period | 4/12/14 → 6/12/14 |
Keywords
- melody segmentation
- repetition detection
- Music Information Retrieval
- music information processing
- digital musicology
- machine learning