You are in : Home » Results & submissions » Select UOA » 11 - Computer Science and Informatics » View submission: Royal Holloway, University of London » Outputs » Detail

Output details

11 - Computer Science and Informatics

Royal Holloway, University of London

Return to search Previous output Next output

Output 0 of 0 in the submission

Output title

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

Type

E - Conference contribution

DOI

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

Name of conference/published proceedings

Artificial Intelligence Applications and Innovations : Proceedings of the 9th IFIP WG 12.5 International Conference, AIAI 2013, Paphos, Cyprus, September 30 – October 2, 2013

Volume number

412

Issue number

First page of article

235

ISSN of proceedings

1868-4238

Year of publication

2013

URL

http://link.springer.com/chapter/10.1007/978-3-642-41142-7_24

Number of additional authors

Additional information

<24>This paper resolves the problem of reverse-engineering playlists from recordings of music programmes and finding (intentionally obfuscated) boundaries between different tracks. Quantitative analysis of trance music is difficult due to its self-similarity; existing methods of detecting transition points were thus not applicable. Therefore a new method based on optimisation reducing to dynamic programming has been developed.

Interdisciplinary

Cross-referral requested

Research group

B - Computer Learning Research Centre

Citation count

Proposed double-weighted

Double-weighted statement

Reserve for a double-weighted output

Non-English

English abstract