Abstract
A method is presented to extract musical features from melodic material. Various viewpoints are defined to focus on complementary aspects of the material. To model the melodic context, two measures of entropy are employed: A set of trained probabilistic models capture local structures via the information-theoretic notion of unpredictability, and an alternative entropy-measure based on adaptive coding is developed to reflect phrasing or motifs. A collection of popular music, in the form of MIDI-files, is analysed using the entropy-measures and techniques from pattern-recognition. To visualise the topology of the ‘tune-space’, a self-organising map is trained with the extracted feature-parameters, leading to the Tune Map.
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Skovenborg, E., Arnspang, J. (2004). Extraction of Structural Patterns in Popular Melodies. In: Wiil, U.K. (eds) Computer Music Modeling and Retrieval. CMMR 2003. Lecture Notes in Computer Science, vol 2771. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39900-1_11
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DOI: https://doi.org/10.1007/978-3-540-39900-1_11
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