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Music genre profiling based on Fisher manifolds and Probabilistic Quantum Clustering

Casaña-Eslava, RV, Jarman, IH, Ortega-Martorell, S, Lisboa, PJG and Martín-Guerrero, JD (2020) Music genre profiling based on Fisher manifolds and Probabilistic Quantum Clustering. Neural Computing and Applications. ISSN 0941-0643

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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.

Item Type: Article
Uncontrolled Keywords: 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
Subjects: M Music and Books on Music > M Music
Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Computer Science & Mathematics
Publisher: Springer
Date Deposited: 13 Nov 2020 14:10
Last Modified: 04 Sep 2021 06:23
DOI or ID number: 10.1007/s00521-020-05499-x
URI: https://researchonline.ljmu.ac.uk/id/eprint/14004
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