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• W41713251 abstract "We present new methods for computing inter-song similarities using intersections between multiple audio pieces. The intersection contains portions that are similar, when one song is a derivative work of the other for example, in two different musical recordings. To scale our search to large song databases we have developed an algorithm based on localitysensitive hashing (LSH) of sequences of audio features called audio shingles. LSH provides an efficient means to identify approximate nearest neighbors in a high-dimensional feature space. We combine these nearest neighbor estimates, each a match from a very large database of audio to a small portion of the query song, to form a measure of the approximate similarity. We demonstrate the utility of our methods on a derivative works retrieval experiment using both exact and approximate (LSH) methods. The results show that LSH is at least an order of magnitude faster than the exact nearest neighbor method and that accuracy is not impacted by the approximate method." @default.
• W41713251 created "2016-06-24" @default.
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• W41713251 date "2006-01-01" @default.
• W41713251 modified "2023-10-01" @default.
• W41713251 title "Song Intersection by Approximate Nearest Neighbor Search." @default.
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