Output details
13 - Electrical and Electronic Engineering, Metallurgy and Materials
Queen Mary University of London : A - Electrical and Electronic engineering
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Output 0 of 0 in the submission
Article title
Music Information Retrieval Using Social Tags and Audio
Type
D - Journal article
Title of journal
IEEE Transactions on Multimedia
Article number
-
Volume number
11
Issue number
3
First page of article
383
ISSN of journal
1520-9210
Year of publication
2009
Number of additional authors
1
Additional information
Paper is one of the earliest marrying text-based with content-based Information Retrieval for musical audio. It demonstrates that where tracks have little or no textual metadata (are not popular, from little-known artists, long tail), they can still be reliably retrieved. Paper was early outcome of OMRAS2 one of EPSRC's first ICT Large Grants (EP/E017614/1) that produced over 120 papers (omras2.org), collaborative between Queen Mary and Goldsmiths. It led to Levy's employment with last.fm, the world leader in social tagging of music, to 3 research internships with the company and subsequent research on automatic ontology generation (DOI: 10.1109/TASL.2013.2263801).
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
