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Digital Humanities Lab

Music Information Retrieval

MUSIVA – Modelling musical similarity over time through the variation principle


Project Leader: Anja Volk
Financier: VIDI
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The aim of this project is to deliver a cognition-based computational model on music similarity that grounds in the variation principle employed in classical, folk and popular music. The project will integrate knowledge and methods from Music Information Retrieval, Musicology and Cognitive Science.

This project investigates the fundamental principle of variation in music studied in Musicology and Cognitive Science as a means to establish similarity. The project researches computational modelling of music similarity in the symbolic domain (i.e. using perception-related notation, such as MIDI or **kern) based on the variation of structural elements (such as motives, rhythms and chord sequences). Specifically, it will take into account the interaction between global and local features of the music and will address music as unfolding in time.

The project aims to deliver a model of music similarity that covers three major styles, namely classical, folk and popular music. The envisioned model will be based on cognitive and structural aspects of music, addressing high-level processes in establishing similarity. Hence, the project will make a major step towards cognition-based similarity models in music, which are urgently needed for the design of meaningful music retrieval systems. The development of a theoretic framework for similarity in music will contribute to the search for general principles of similarity across different domains envisioned in Cognitive Science.