Tim Scarfe, Wouter M. Koolen and Yuri Kalniskan Artificial Intelligence Applications and Innovations. Springer Berlin Heidelberg, 2013. 235-244. Get Involved! Code available @ https://github.com/ecsplendid/DanceMusicSegmentation Note that this paper has been superseded by the new version @ /papers/segmentationextended In this paper we describe an unsupervised, deterministic algorithm for segmenting DJ-mixed Electronic Dance Music (EDM) streams (for example; podcasts, radio shows, live events) into their respective tracks. We attempt to reconstruct boundaries as close as possible to what a human domain expert would engender. The goal of DJ-mixing is to render track boundaries effectively invisible from the standpoint of human perception which makes the problem difficult. We use Dynamic Programming (DP) to optimally segment a cost matrix derived from a similarity matrix. The similarity matrix is based on the cosines of a time series of kernel-transformed Fourier based features designed with this domain in mind. Our method is applied to EDM streams. Its formulation incorporates long-term self similarity as a first class concept combined with DP and it is qualitatively assessed on a large corpus of long streams that have been hand labelled by a domain expert.  This paper is published in the Springer collection Artificial Intelligence Applications and Innovations 2013 -- http://link.springer.com/chapter/10.1007/978-3-642-41142-7_24. @incollection{scarfe2013long, title={A Long-Range Self-similarity Approach to Segmenting DJ Mixed Music Streams}, author={Scarfe, Tim and Koolen, Wouter M and Kalnishkan, Yuri}, booktitle={Artificial Intelligence Applications and Innovations}, pages={235--244}, year={2013}, publisher={Springer} } |
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