Querying Large Collections of Music for Similarity

杨利霞

Querying Large Collections of Music for Similarity

We present a system capable of performing similarity
queries against a large archive of digital music. Users are
able to search for songs which “sound similar” to a given
query song, thereby aiding the navigation and discovery
of new music in such an archive. Our technique is based
on reduction of the music data to a feature space of rela
tively small dimensionality (1248 feature dimensions per
song); this is accomplished using a set of feature extrac
tors which derive frequency, amplitude, and tempo data
from the encoded music data. Queries are then per
formed using a k-nearest neighbor search in the feature
space. Our system allows subsets of the feature space
to be selected on a per-query basis.
We have integrated the music query engine into an
online MP3 music archive consisting of over 7000 songs.
We present an evaluation of our feature extraction and
query results against this archive.