Casey, Michael A. and Slaney, M.. 2007. 'Fast Recognition of Remixed Music Audio'. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). HI, United States. [Conference or Workshop Item]

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Abstract or Description

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.

Item Type:	Conference or Workshop Item (Paper)
Identification Number (DOI):	https://doi.org/10.1109/ICASSP.2007.367347
Departments, Centres and Research Units:	Computing
Dates:	Date Event 2007 Published
Event Location:	HI, United States
Item ID:	15157
Date Deposited:	01 Dec 2015 14:53
Last Modified:	20 Jun 2017 09:42
URI:	https://research.gold.ac.uk/id/eprint/15157

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