A Long-Range Self-similarity Approach to Segmenting DJ Mixed Music Streams

Abstract : 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.
Type de document :
Communication dans un congrès
Harris Papadopoulos; Andreas S. Andreou; Lazaros Iliadis; Ilias Maglogiannis. 9th Artificial Intelligence Applications and Innovations (AIAI), Sep 2013, Paphos, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-412, pp.235-244, 2013, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-41142-7_24〉
Liste complète des métadonnées

Littérature citée [16 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01459615
Contributeur : Hal Ifip <>
Soumis le : mardi 7 février 2017 - 13:05:05
Dernière modification le : vendredi 1 décembre 2017 - 01:16:35
Document(s) archivé(s) le : lundi 8 mai 2017 - 14:11:48

Fichier

978-3-642-41142-7_24_Chapter.p...
Fichiers produits par l'(les) auteur(s)

Licence


Distributed under a Creative Commons Paternité 4.0 International License

Identifiants

Citation

Tim Scarfe, Wouter Koolen, Yuri Kalnishkan. A Long-Range Self-similarity Approach to Segmenting DJ Mixed Music Streams. Harris Papadopoulos; Andreas S. Andreou; Lazaros Iliadis; Ilias Maglogiannis. 9th Artificial Intelligence Applications and Innovations (AIAI), Sep 2013, Paphos, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-412, pp.235-244, 2013, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-41142-7_24〉. 〈hal-01459615〉

Partager

Métriques

Consultations de la notice

38

Téléchargements de fichiers

63