Dominik Schnitzer, Arthur Flexer, Gerhard Widmer,
"A Filter-and Refine Indexing Method for Fast Similarity Search in Millions of Music Tracks."
: Proceedings of the 10th International Conference on Music Information Retrieval (ISMIR 2009), 2009
A Filter-and Refine Indexing Method for Fast Similarity Search in Millions of Music Tracks.
Sprache des Titels:
Proceedings of the 10th International Conference on Music Information Retrieval (ISMIR 2009)
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.