Exploring Melodic Contour

Bibliographic Data

ID21557237
AuthorsMichal Goldstein (New York University), Michal N Goldstein (New York University), Roni Granot (0000-0002-0711-8322, The Hebrew University of Jerusalem, Jerusalem, Israel), Pablo Ripollés (0000-0002-8463-3723, New York University), Morwaread M Farbood (New York University), Morwaread Farbood (0000-0003-3622-7488, New York University)
Year2025
Volume42
Issue3
Pages225-241
Publication date2025-02-01
Open AccessNo
TypeARTICLE
VenueMusic Perception An Interdisciplinary Journal (JOURNAL)
Journal identifiersISSN: 0730-7829 • E-ISSN: 1533-8312
PublisherUniversity of California Press (PUBLISHER • US)
DOI10.1525/mp.2024.aa003
OpenAlexW4405587246
LanguageEN
References cited28

Previous studies investigating common melodic contour shapes have relied on methodologies that require prior assumptions regarding the expected contour patterns. Here, a new approach for examining contour using dimensionality reduction and unsupervised machine-learning clustering methods is presented. This new methodology was tested across four sets of data: two sets of European folk songs; a mixed-style, curated dataset of Western music; and a set of Chinese folk songs. In general, the results suggest the presence of four broad common contour shapes across datasets: convex, concave, descending, and ascending. In addition, the analysis revealed some micro-contour tendencies, such as pitch stability at the beginning of phrases and descending pitch at phrase endings. These results are in line with previous studies of melodic contour and provide new insights regarding the prevalent contour characteristics in Western music

Art · Melody · Visual arts · Computer Science · Music and Audio Processing · Music Technology and Sound Studies · Neuroscience and Music Perception · Artificial Intelligence

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