On Competitiveness of Nearest-Neighbor Based Music Classification: A Methodological Critique

Haukur Pálmasson 1 Björn Þór Jónsson 2, 1 Laurent Amsaleg 3 Markus Schedl 4 Peter Knees 5
1 School of Computer Science [Reykjavik]
2 IT University of Copenhagen
3 LinkMedia - Creating and exploiting explicit links between multimedia fragments
Inria Rennes – Bretagne Atlantique , IRISA_D6 - MEDIA ET INTERACTIONS
4 Johannes Kepler Universität Linz
5 TU Wien - Technische Universität Wien
Abstract : The traditional role of nearest-neighbor classification in music classification research is that of a straw man opponent for the learning approach of the hour. Recent work in high-dimensional indexing has shown that approximate nearest-neighbor algorithms are extremely scalable, yielding results of reasonable quality from billions of high-dimensional features. With such efficient large-scale classifiers, the traditional music classification methodology of reducing both feature dimensionality and feature quantity is incorrect; instead the approximate nearest-neighbor classifier should be given an extensive data collection to work with. We present a case study, using a well-known MIR classification benchmark with well-known music features, which shows that a simple nearest-neighbor classifier performs very competitively when given ample data. In this position paper, we therefore argue that nearest-neighbor classification has been treated unfairly in the literature and may be much more competitive than previously thought.
Type de document :
Communication dans un congrès
10th International Conference on Similarity Search and Applications, Oct 2017, munich, Germany. 10th International Conference on Similarity Search and Applications, 2017, 10th International Conference on Similarity Search and Applications
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https://hal.archives-ouvertes.fr/hal-01565217
Contributeur : Laurent Amsaleg <>
Soumis le : mercredi 19 juillet 2017 - 16:00:29
Dernière modification le : vendredi 22 mars 2019 - 16:46:03

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  • HAL Id : hal-01565217, version 1

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UNIV-RENNES1 | IRISA | INRIA2017 | INRIA | UR1-UFR-ISTIC | UNIV-RENNES

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Haukur Pálmasson, Björn Þór Jónsson, Laurent Amsaleg, Markus Schedl, Peter Knees. On Competitiveness of Nearest-Neighbor Based Music Classification: A Methodological Critique. 10th International Conference on Similarity Search and Applications, Oct 2017, munich, Germany. 10th International Conference on Similarity Search and Applications, 2017, 10th International Conference on Similarity Search and Applications. 〈hal-01565217〉

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