A long-range self-similarity approach to segmenting DJ mixed music streams. / Scarfe, Tim; Koolen, Wouter M.; Kalnishkan, Yuri.

Artificial Intelligence Applications and Innovations: Proceedings of the 9th IFIP WG 12.5 International Conference, AIAI 2013, Paphos, Cyprus, September 30 – October 2, 2013. ed. / Harris Papadopoulos; Andreas S. Andreou; Lazaros Iliadis; Ilias Maglogiannis. Springer, 2013. p. 235-244 (IFIP Advances in Information and Communication Technology; Vol. 412).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Published

Standard

A long-range self-similarity approach to segmenting DJ mixed music streams. / Scarfe, Tim; Koolen, Wouter M.; Kalnishkan, Yuri.

Artificial Intelligence Applications and Innovations: Proceedings of the 9th IFIP WG 12.5 International Conference, AIAI 2013, Paphos, Cyprus, September 30 – October 2, 2013. ed. / Harris Papadopoulos; Andreas S. Andreou; Lazaros Iliadis; Ilias Maglogiannis. Springer, 2013. p. 235-244 (IFIP Advances in Information and Communication Technology; Vol. 412).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Scarfe, T, Koolen, WM & Kalnishkan, Y 2013, A long-range self-similarity approach to segmenting DJ mixed music streams. in H Papadopoulos, AS Andreou, L Iliadis & I Maglogiannis (eds), Artificial Intelligence Applications and Innovations: Proceedings of the 9th IFIP WG 12.5 International Conference, AIAI 2013, Paphos, Cyprus, September 30 – October 2, 2013. IFIP Advances in Information and Communication Technology, vol. 412, Springer, pp. 235-244, 9th IFIP International Conference, AIAI 2013, Paphos, Cyprus, 30/09/13. https://doi.org/10.1007/978-3-642-41142-7_24

APA

Scarfe, T., Koolen, W. M., & Kalnishkan, Y. (2013). A long-range self-similarity approach to segmenting DJ mixed music streams. In H. Papadopoulos, A. S. Andreou, L. Iliadis, & I. Maglogiannis (Eds.), Artificial Intelligence Applications and Innovations: Proceedings of the 9th IFIP WG 12.5 International Conference, AIAI 2013, Paphos, Cyprus, September 30 – October 2, 2013 (pp. 235-244). (IFIP Advances in Information and Communication Technology; Vol. 412). Springer. https://doi.org/10.1007/978-3-642-41142-7_24

Vancouver

Scarfe T, Koolen WM, Kalnishkan Y. A long-range self-similarity approach to segmenting DJ mixed music streams. In Papadopoulos H, Andreou AS, Iliadis L, Maglogiannis I, editors, Artificial Intelligence Applications and Innovations: Proceedings of the 9th IFIP WG 12.5 International Conference, AIAI 2013, Paphos, Cyprus, September 30 – October 2, 2013. Springer. 2013. p. 235-244. (IFIP Advances in Information and Communication Technology). https://doi.org/10.1007/978-3-642-41142-7_24

Author

Scarfe, Tim ; Koolen, Wouter M. ; Kalnishkan, Yuri. / A long-range self-similarity approach to segmenting DJ mixed music streams. Artificial Intelligence Applications and Innovations: Proceedings of the 9th IFIP WG 12.5 International Conference, AIAI 2013, Paphos, Cyprus, September 30 – October 2, 2013. editor / Harris Papadopoulos ; Andreas S. Andreou ; Lazaros Iliadis ; Ilias Maglogiannis. Springer, 2013. pp. 235-244 (IFIP Advances in Information and Communication Technology).

BibTeX

@inproceedings{d4a3bd733a5d4d40a2b5dd575013e2ab,
title = "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.",
keywords = "Music, SEGMENTATION, DJ mix, dynamic programming",
author = "Tim Scarfe and Koolen, {Wouter M.} and Yuri Kalnishkan",
year = "2013",
doi = "10.1007/978-3-642-41142-7_24",
language = "English",
isbn = "978-3-642-41141-0",
series = "IFIP Advances in Information and Communication Technology",
publisher = "Springer",
pages = "235--244",
editor = "Harris Papadopoulos and Andreou, {Andreas S.} and Lazaros Iliadis and Ilias Maglogiannis",
booktitle = "Artificial Intelligence Applications and Innovations",
note = "9th IFIP International Conference, AIAI 2013 ; Conference date: 30-09-2013 Through 02-10-2013",

}

RIS

TY - GEN

T1 - A long-range self-similarity approach to segmenting DJ mixed music streams

AU - Scarfe, Tim

AU - Koolen, Wouter M.

AU - Kalnishkan, Yuri

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - Music

KW - SEGMENTATION

KW - DJ mix

KW - dynamic programming

U2 - 10.1007/978-3-642-41142-7_24

DO - 10.1007/978-3-642-41142-7_24

M3 - Conference contribution

SN - 978-3-642-41141-0

T3 - IFIP Advances in Information and Communication Technology

SP - 235

EP - 244

BT - Artificial Intelligence Applications and Innovations

A2 - Papadopoulos, Harris

A2 - Andreou, Andreas S.

A2 - Iliadis, Lazaros

A2 - Maglogiannis, Ilias

PB - Springer

T2 - 9th IFIP International Conference, AIAI 2013

Y2 - 30 September 2013 through 2 October 2013

ER -