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.
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Mokbel, B., Hasenfuss, A., Hammer, B. (2009). Graph-Based Representation of Symbolic Musical Data. In: Torsello, A., Escolano, F., Brun, L. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2009. Lecture Notes in Computer Science, vol 5534. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02124-4_5
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DOI: https://doi.org/10.1007/978-3-642-02124-4_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02123-7
Online ISBN: 978-3-642-02124-4
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