Music Information Retrieval; Cover Song Identification; Rank Aggregation
Abstract :
[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.
Disciplines :
Computer science
Author, co-author :
Osmalsky, Julien ; Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Dép. d'électric., électron. et informat. (Inst.Montefiore)
Van Droogenbroeck, Marc ; Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
Embrechts, Jean-Jacques ; Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Techniques du son et de l'image
Language :
English
Title :
Enhancing Cover Song Identification with Hierarchical Rank Aggregation
Publication date :
August 2016
Event name :
17th International Society for Music Information Retrieval Conference
Event place :
New York, United States
Event date :
August 7-11, 2016
Audience :
International
Main work title :
Proceedings of the 17th International for Music Information Retrieval Conference
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