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Conference papers
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.
https://hal.archives-ouvertes.fr/hal-01565217
Contributor : Laurent Amsaleg <>
Submitted on : Wednesday, July 19, 2017 - 4:00:29 PM
Last modification on : Sunday, April 26, 2020 - 1:34:02 PM
Contributor : Laurent Amsaleg <>
Submitted on : Wednesday, July 19, 2017 - 4:00:29 PM
Last modification on : Sunday, April 26, 2020 - 1:34:02 PM
