SISAP 2017: On Competitiveness of Nearest-Neighbor-Based Music Classification: A Methodological Critique
Authors: Haukur Pálmason, Björn Þór Jónsson, Laurent Amsaleg, Markus Schedl, and Peter Knees
Björn Þór Jónsson is presenting a paper at the SISAP conference (http://www.sisap.org/2017/) in München.
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.