Semantic Similarity for Music Retrieval

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Semantic Similarity for Music Retrieval
Luke Barrington, Doug Turnbull, David Torres
Gert Lanckriet Electrical Computer Engineering
University of California, San Diego lbarrington_at_uc
sd.edu
Audio Text Features
Our models are trained on the CAL500 dataset, a
heterogeneous data set of song / caption
pairs 500 popular western songs, 146-word
vocabulary Each track has been annotated by at
least 3 humans Audio content is represented as a
bag of feature vectors MFCC features plus 1st
and 2nd time deltas 10,000 feature vectors
per minute of audio Annotations are represented
as a bag of words Binary document vector of
length 146