Graph-Based Representation of Symbolic Musical Data

Authors:
Bassam Mokbel;Alexander Hasenfuss;Barbara Hammer
Affiliations:
Department of Computer Science, Clausthal University of Technology, Clausthal-Zellerfeld, Germany;Department of Computer Science, Clausthal University of Technology, Clausthal-Zellerfeld, Germany;Department of Computer Science, Clausthal University of Technology, Clausthal-Zellerfeld, Germany
Venue:
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Year:
2009

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Abstract

In this work, we present an approach that utilizes a graph-based representation of symbolic musical data in the context of automatic topographic mapping. A novel approach is introduced that represents melodic progressions as graph structures providing a dissimilarity measure which complies with the invariances in the human perception of melodies. That way, music collections can be processed by non-Euclidean variants of Neural Gas or Self-Organizing Maps for clustering, classification, or topographic mapping for visualization. We demonstrate the performance of the technique on several datasets of classical music.