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Neural Computing and Applications
Published in: Neural Computing and Applications 13/2021

12-11-2020 | Original Article

Music genre profiling based on Fisher manifolds and Probabilistic Quantum Clustering

Authors: Raúl V. Casaña-Eslava, Ian H. Jarman, Sandra Ortega-Martorell, Paulo J. G. Lisboa, José D. Martín-Guerrero

Published in: Neural Computing and Applications | Issue 13/2021

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Abstract

Probabilistic classifiers induce a similarity metric at each location in the space of the data. This is measured by the Fisher Information Matrix. Pairwise distances in this Riemannian space, calculated along geodesic paths, can be used to generate a similarity map of the data. The novelty in the paper is twofold; to improve the methodology for visualisation of data structures in low-dimensional manifolds, and to illustrate the value of inferring the structure from a probabilistic classifier by metric learning, through application to music data. This leads to the discovery of new structures and song similarities beyond the original genre classification labels. These similarities are not directly observable by measuring Euclidean distances between features of the original space, but require the correct metric to reflect similarity based on genre. The results quantify the extent to which music from bands typically associated with one particular genre can, in fact, crossover strongly to another genre.
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Appendix
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Footnotes
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Metadata
Title
Music genre profiling based on Fisher manifolds and Probabilistic Quantum Clustering
Authors
Raúl V. Casaña-Eslava
Ian H. Jarman
Sandra Ortega-Martorell
Paulo J. G. Lisboa
José D. Martín-Guerrero
Publication date
12-11-2020
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 13/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05499-x

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