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Content Provider | IEEE Xplore Digital Library |
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Author | Casey, M. Slaney, M. |
Copyright Year | 2007 |
Description | Author affiliation: Dept. of Comput., London Univ., UK (Casey, M.) |
Abstract | We present an efficient algorithm for automatically detecting remixes of pop songs in large commercial collections. Remixes are closely related as commercial products but they are not closely related in their audio spectral content because of the nature of the remixing process. Therefore spectral modelling approaches to audio similarity fail to recognize them. We propose a new approach - that chops songs into small chunks called audio shingles - to recognize remixed songs. We model the distribution of pair-wise distances between shingles by two independent processes - one corresponding to remix content and the other corresponding to non-remix content in a database. A nearest neighbour algorithm groups songs if they share shingles drawn from the remix process. Our results show 1) log-chromagram shingles separate remixed from non-remixed content with 75%-75% precision-recall performance, cepstral coefficient features do not separate the two distributions adequately 2) increasing the observations from the remix distribution increases the separability. Efficient implementation follows from the separability of the distributions using locality sensitive hashing (LSH) which speeds up automatic grouping of remixes by between one to two orders of magnitude in a 2018-song test set. |
File Size | 142740 |
File Format | |
ISBN | 1424407273 |
ISSN | 15206149 |
DOI | 10.1109/ICASSP.2007.367347 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2007-04-15 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Fingerprint recognition Multiple signal classification Spatial databases Cepstral analysis Automatic testing Statistical distributions Radio navigation Marketing and sales Web sites Search engines Databases Music Statistics LSH Shingles |
Content Type | Text |
Resource Type | Article |
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