Cover song identification with metric learning using distance as a feature

Hoon Heo, Hyun Woo Kim, Wan Soo Kim, Kyogu Lee

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

2 Citations (Scopus)

Abstract

Most of cover song identification algorithms are based on the pairwise (dis)similarity between two songs which are represented by harmonic features such as chroma, and therefore the choice of a distance measure and a feature has a significant impact on performance. Furthermore, since the similarity measure is query-dependent, it cannot represent an absolute distance measure. In this paper, we present a novel approach to tackle the cover song identification problem from a new perspective. We first construct a set of core songs, and represent each song in a high-dimensional space where each dimension indicates the pairwise distance between the given song and the other in the pre-defined core set. There are several advantages to this. First, using a number of reference songs in the core set, we make the most of relative distances to many other songs. Second, as all songs are transformed into the same high-dimensional space, kernel methods and metric learning are exploited for distance computation. Third, our approach does not depend on the computation method for the pairwise distance, and thus can use any existing algorithms. Experimental results confirm that the proposed approach achieved a large performance gain compared to the state-of-the-art methods.

Original languageEnglish
Title of host publicationProceedings of the 18th International Society for Music Information Retrieval Conference, ISMIR 2017
EditorsZhiyao Duan, Douglas Turnbull, Xiao Hu, Sally Jo Cunningham
PublisherInternational Society for Music Information Retrieval
Pages628-634
Number of pages7
ISBN (Electronic)9789811151798
Publication statusPublished - 2017 Jan 1
Externally publishedYes
Event18th International Society for Music Information Retrieval Conference, ISMIR 2017 - Suzhou, China
Duration: 2017 Oct 232017 Oct 27

Publication series

NameProceedings of the 18th International Society for Music Information Retrieval Conference, ISMIR 2017

Conference

Conference18th International Society for Music Information Retrieval Conference, ISMIR 2017
CountryChina
CitySuzhou
Period17/10/2317/10/27

ASJC Scopus subject areas

  • Music
  • Information Systems

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  • Cite this

    Heo, H., Kim, H. W., Kim, W. S., & Lee, K. (2017). Cover song identification with metric learning using distance as a feature. In Z. Duan, D. Turnbull, X. Hu, & S. J. Cunningham (Eds.), Proceedings of the 18th International Society for Music Information Retrieval Conference, ISMIR 2017 (pp. 628-634). (Proceedings of the 18th International Society for Music Information Retrieval Conference, ISMIR 2017). International Society for Music Information Retrieval.