A long-range self-similarity approach to segmenting DJ mixed music streams

Tim Scarfe, Wouter M. Koolen, Yuri Kalnishkan

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In this paper we describe an unsupervised, deterministic algorithm for segmenting DJ-mixed Electronic Dance Music 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.
Original languageEnglish
Title of host publicationArtificial Intelligence Applications and Innovations
Subtitle of host publicationProceedings of the 9th IFIP WG 12.5 International Conference, AIAI 2013, Paphos, Cyprus, September 30 – October 2, 2013
EditorsHarris Papadopoulos, Andreas S. Andreou, Lazaros Iliadis, Ilias Maglogiannis
Number of pages10
ISBN (Electronic)978-3-642-41142-7
ISBN (Print)978-3-642-41141-0
Publication statusPublished - 2013
Event9th IFIP International Conference, AIAI 2013 - Paphos, Cyprus
Duration: 30 Sept 20132 Oct 2013

Publication series

NameIFIP Advances in Information and Communication Technology
ISSN (Print)1868-4238


Conference9th IFIP International Conference, AIAI 2013


  • Music
  • DJ mix
  • dynamic programming

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