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

Haukur Pálmason, Björn Thór Jónsson, Laurent Amsaleg, Markus Schedl, Peter Knees

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


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 aggregating and compressing the audio features 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.
Original languageEnglish
Title of host publicationProceedings of the International Conference on Similarity Search and Applications (SISAP)
EditorsChristian Beecks, Felix Borutta, Peer Kröger, Thomas Seidl
Place of PublicationMunich, Germany
Publication dateOct 2017
ISBN (Print)978-3-319-68473-4
ISBN (Electronic)978-3-319-68474-1
Publication statusPublished - Oct 2017
SeriesLecture Notes in Computer Science


  • Music classification
  • Approximate nearest-neighbor classifiers
  • Research methodology


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