Crossing Phrase Boundaries in Music
Cenkerová, Z., Hartmann, M., & Toiviainen, P. (2018). Crossing Phrase Boundaries in Music. In A. Georgaki, & A. Andreopoulou (Eds.), SMC 2018 : Proceedings of the 15th Sound and Music Computing Conference 2018 (pp. 66-71). Proceedings of the Sound and Music Computing Conferences. http://doi.org/10.5281/zenodo.1422505
Date
2018Copyright
© Authors, 2018
This paper presents a new model for segmenting symbolic
music data into phrases. It is based on the idea that
melodic phrases tend to consist of notes, which increase
rather than decrease in length towards the phrase end.
Previous research implies that the timing of note events
might be a stronger predictor of both theoretical and
perceived segmentation than pitch information. Our approach
therefore relies only on temporal information
about note onsets. Phrase boundaries are predicted at
those points in a melody where the difference between
subsequent note-to-note intervals reaches minimal values.
On its own, the proposed model is parameter-free, does
not require adjustments to fit a particular dataset, and is
not biased towards metrical music. We have tested the
model on a set of 6226 songs and compared it with existing
rule-based segmentation algorithms that had been
previously identified as good performers: LBDM and
Grouper. Next, we investigated two additional predictors:
meter and the presence of pauses. Finally, we integrated
all approaches into a meta-classifier, which yielded a
significantly better performance than each of the individual
models.
...
Parent publication ISBN
978-9963-697-30-4Conference
Sound and Music Computing ConferenceIs part of publication
SMC 2018 : Proceedings of the 15th Sound and Music Computing Conference 2018ISSN Search the Publication Forum
2518-3672
Original source
http://doi.org/10.5281/zenodo.1422505Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/28644736
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