Reference : Enhancing Cover Song Identification with Hierarchical Rank Aggregation
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/196764
Enhancing Cover Song Identification with Hierarchical Rank Aggregation
English
Osmalsky, Julien mailto [Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Dép. d'électric., électron. et informat. (Inst.Montefiore) >]
Van Droogenbroeck, Marc mailto [Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications >]
Embrechts, Jean-Jacques mailto [Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Techniques du son et de l'image >]
Aug-2016
Proceedings of the 17th International for Music Information Retrieval Conference
136-142
Yes
No
International
17th International Society for Music Information Retrieval Conference
August 7-11, 2016
New York
USA
[en] Music Information Retrieval ; Cover Song Identification ; Rank Aggregation
[en] Abstract Cover song identification involves calculating pairwise similarities between a query audio track and a database of reference tracks. While most authors make exclusively use of chroma features, recent work tends to demonstrate that combining similarity estimators based on multiple audio features increases the performance. We improve this approach by using a hierarchical rank aggregation method for combining estimators based on different features. More precisely, we first aggregate estimators based on global features such as the tempo, the duration, the loudness, the beats, and the average chroma vectors. Then, we aggregate the resulting composite estimator with four popular state-of-the-art methods based on chromas as well as timbre sequences. We further introduce a refinement step for the rank aggregation called “local Kemenization” and quantify its benefit for cover song identification. The performance of our method is evaluated on the Second Hand Song dataset. Our experiments show an significant improvement of the performance, up to an increase of more than 200 % of the number of queries identified in the Top-1, compared to previous results.
Researchers ; Professionals ; Students ; General public
http://hdl.handle.net/2268/196764

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