Musical Feature and Novelty Curve Characterizations as Predictors of Segmentation Accuracy
Hartmann, M., Lartillot, O., & Toiviainen, P. (2017). Musical Feature and Novelty Curve Characterizations as Predictors of Segmentation Accuracy. In T. Lokki, J. Pätynen, & V. Välimäki (Eds.), SMC 2017 : Proceedings of the 14th Sound and Music Computing Conference 2017 (pp. 365-372). Aalto-yliopisto. Proceedings of the Sound and Music Computing Conferences. http://smc2017.aalto.fi/
Julkaistu sarjassa
Proceedings of the Sound and Music Computing ConferencesPäivämäärä
2017Tekijänoikeudet
© 2017 Martin Hartmann et al. This is an open-access article distributed
under the terms of the Creative Commons Attribution 3.0 Unported License.
Novelty detection is a well-established method for analyzing
the structure of music based on acoustic descriptors.
Work on novelty-based segmentation prediction has mainly
concentrated on enhancement of features and similarity
matrices, novelty kernel computation and peak detection.
Less attention, however, has been paid to characteristics
of musical features and novelty curves, and their contribution
to segmentation accuracy. This is particularly important
as it can help unearth acoustic cues prompting perceptual
segmentation and find new determinants of segmentation
model performance. This study focused on spectral,
rhythmic and harmonic prediction of perceptual segmentation
density, which was obtained for six musical examples
from 18 musician listeners via an annotation task. The
proposed approach involved comparisons between perceptual
segment density and novelty curves; in particular, we
investigated possible predictors of segmentation accuracy
based on musical features and novelty curves. For pitch
and rhythm, we found positive correlates between segmentation
accuracy and both local variability of musical features
and mean distance between subsequent local maxima
of novelty curves. According to the results, segmentation
accuracy increases for stimuli with milder local changes
and fewer novelty peaks. Implications regarding prediction
of listeners’ segmentation are discussed in the light of theoretical
postulates of perceptual organization.
...
Julkaisija
Aalto-yliopistoEmojulkaisun ISBN
978-952-60-3729-5Konferenssi
Sound and Music Computing ConferenceKuuluu julkaisuun
SMC 2017 : Proceedings of the 14th Sound and Music Computing Conference 2017ISSN Hae Julkaisufoorumista
2518-3672Asiasanat
Alkuperäislähde
http://smc2017.aalto.fi/Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/27114119
Metadata
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Lisenssi
Ellei muuten mainita, aineiston lisenssi on © 2017 Martin Hartmann et al. This is an open-access article distributed
under the terms of the Creative Commons Attribution 3.0 Unported License.
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