"Learning Binary Codes for Efficient Large-Scale Music Similarity Search"
: Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR), 11-2013
Learning Binary Codes for Efficient Large-Scale Music Similarity Search
Sprache des Titels:
Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR)
Content-based music similarity estimation provides a
way to find songs in the unpopular ?long tail? of commercial catalogs. However, state-of-the-art music similarity measures are too slow to apply to large databases, as
they are based on finding nearest neighbors among very
high-dimensional or non-vector song representations that
are difficult to index.
In this work, we adopt recent machine learning methods
to map such song representations to binary codes. A linear scan over the codes quickly finds a small set of likely
neighbors for a query to be refined with the original expensive similarity measure. Although search costs grow linearly with the collection size, we show that for commercialscale databases and two state-of-the-art similarity measures,
this outperforms five previous attempts at approximate nearest neighbor search. When required to return 90% of true
nearest neighbors, our method is expected to answer 4.2
1-NN queries or 1.3 50-NN queries per second on a collection of 30 million songs using a single CPU core; an up to
260 fold speedup over a full scan of 90% of the database.