HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
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.
Document type :
Conference papers
Complete list of metadata

Contributor : Laurent Amsaleg Connect in order to contact the contributor
Submitted on : Wednesday, July 19, 2017 - 4:00:29 PM
Last modification on : Friday, April 8, 2022 - 4:08:03 PM


  • 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⟩



Record views