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H. Pálmason, B. Jónsson, L. Amsaleg, M. Schedl, P. Knees:
"On Competitiveness of Nearest-Neighbor Based Music Classification: A Methodological Critique";
Talk: 10th International Conference on Similarity Search and Applications, Munich, Germany; 2017-10-04 - 2017-10-06; in: "Similarity Search and Applications", C. Beeck, F. Borutta, P. Kröger, T. Seidl (ed.); Lecture Notes in Computer Science, Springer, 10609 (2017), ISBN: 978-3-319-68473-4; 275 - 283.

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.

Music classification, Approximate nearest-neighbor classifiers, Research methodology

http://dx.doi.org/10.1007/978-3-319-68474-1_19

Project Head Peter Knees:
SmarterJam