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

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.
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Contributor : Laurent Amsaleg <>
Submitted on : Wednesday, July 19, 2017 - 4:00:29 PM
Last modification on : Monday, November 16, 2020 - 9:34:04 AM


  • HAL Id : hal-01565217, version 1


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. ⟨hal-01565217⟩



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