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OFAI-TR-2009-03 ( 168kB PDF file)

A Filter-and-Refine Indexing Method for Fast Similarity Search in Millions of Music Tracks

Dominik Schnitzer, Arthur Flexer, Gerhard Widmer

We present a filter-and-refine method to speed up acoustic audio similarity queries which use the Kullback-Leibler divergence as similarity measure. The proposed method rescales the divergence and uses a modified FastMap implementation to accelerate nearest-neighbor queries. The search for similar music pieces is accelerated by a factor of 10-30 compared to a linear scan but still offers high recall values (relative to a linear scan) of 95 - 99%. We show how the proposed method can be used to query several million songs for their acoustic neighbors very fast while producing almost the same results that a linear scan over the whole database would return. We present a working prototype implementation which is able to process similarity queries on a 2,5 million songs collection in about half a second on a standard CPU.

Keywords: Music Information Retrieval, Indexing,

Citation: Schnitzer D., Flexer A., Widmer G.: A Filter-and-Refine Indexing Method for Fast Similarity Search in Millions of Music Tracks, in Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR'09), Kobe, Japan, 2009.