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 language | English |
|---|---|
| Title of host publication | Proceedings of the 18th International Society for Music Information Retrieval Conference, ISMIR 2017 |
| Editors | Zhiyao Duan, Douglas Turnbull, Xiao Hu, Sally Jo Cunningham |
| Publisher | International Society for Music Information Retrieval |
| Pages | 628-634 |
| Number of pages | 7 |
| ISBN (Electronic) | 9789811151798 |
| Publication status | Published - 2017 Jan 1 |
| Externally published | Yes |
| Event | 18th International Society for Music Information Retrieval Conference, ISMIR 2017 - Suzhou, China Duration: 2017 Oct 23 → 2017 Oct 27 |
Publication series
| Name | Proceedings of the 18th International Society for Music Information Retrieval Conference, ISMIR 2017 |
|---|
Conference
| Conference | 18th International Society for Music Information Retrieval Conference, ISMIR 2017 |
|---|---|
| Country | China |
| City | Suzhou |
| Period | 17/10/23 → 17/10/27 |
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ASJC Scopus subject areas
- Music
- Information Systems
Cite this
Cover song identification with metric learning using distance as a feature. / Heo, Hoon; Kim, Hyun Woo; Kim, Wan Soo; Lee, Kyogu.
Proceedings of the 18th International Society for Music Information Retrieval Conference, ISMIR 2017. ed. / Zhiyao Duan; Douglas Turnbull; Xiao Hu; Sally Jo Cunningham. International Society for Music Information Retrieval, 2017. p. 628-634 (Proceedings of the 18th International Society for Music Information Retrieval Conference, ISMIR 2017).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Cover song identification with metric learning using distance as a feature
AU - Heo, Hoon
AU - Kim, Hyun Woo
AU - Kim, Wan Soo
AU - Lee, Kyogu
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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.
AB - 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.
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M3 - Conference contribution
T3 - Proceedings of the 18th International Society for Music Information Retrieval Conference, ISMIR 2017
SP - 628
EP - 634
BT - Proceedings of the 18th International Society for Music Information Retrieval Conference, ISMIR 2017
A2 - Duan, Zhiyao
A2 - Turnbull, Douglas
A2 - Hu, Xiao
A2 - Cunningham, Sally Jo
PB - International Society for Music Information Retrieval
ER -