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. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Skovenborg, E., & Arnspang, J. (2004). Extraction of structural patterns in popular melodies. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2771, 98–113. https://doi.org/10.1007/978-3-540-39900-1_11
Mendeley helps you to discover research relevant for your work.