Comunicació presentada a: International Society for Music Information Retrieval Conference celebrat de l'11 al 16 d'octubre de 2020 de manera virtual., Recent works have addressed the automatic cover detection problem from a metric learning perspective. They employ different input representations, aiming to exploit melodic or harmonic characteristics of songs and yield promising performances. In this work, we propose a comparative study of these different representations and show that systems combining melodic and harmonic features drastically outperform those relying on a single input representation. We illustrate how these features complement each other with both quantitative and qualitative analyses. We finally investigate various fusion schemes and propose methods yielding state-of-the-art performances on two publicly-available large datasets., FY is supported by the MIP-Frontiers project, the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 765068, and EG by TROMPA, the Horizon 2020 project 770376-2.
%0 Generic
%1 doras2020combining
%A Doras, Guillaume
%A Yesiler, Furkan
%A Serrà Julià, Joan
%A Gómez Gutiérrez, Emilia, 1975
%A Peeters, Geoffroy
%D 2020
%I International Society for Music Information Retrieval (ISMIR)
%K cover coverdetection detection feature information mir music retrieval
%T Combining musical features for cover detection
%U https://repositori.upf.edu/handle/10230/45719
%X Comunicació presentada a: International Society for Music Information Retrieval Conference celebrat de l'11 al 16 d'octubre de 2020 de manera virtual., Recent works have addressed the automatic cover detection problem from a metric learning perspective. They employ different input representations, aiming to exploit melodic or harmonic characteristics of songs and yield promising performances. In this work, we propose a comparative study of these different representations and show that systems combining melodic and harmonic features drastically outperform those relying on a single input representation. We illustrate how these features complement each other with both quantitative and qualitative analyses. We finally investigate various fusion schemes and propose methods yielding state-of-the-art performances on two publicly-available large datasets., FY is supported by the MIP-Frontiers project, the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 765068, and EG by TROMPA, the Horizon 2020 project 770376-2.
@misc{doras2020combining,
abstract = {Comunicació presentada a: International Society for Music Information Retrieval Conference celebrat de l'11 al 16 d'octubre de 2020 de manera virtual., Recent works have addressed the automatic cover detection problem from a metric learning perspective. They employ different input representations, aiming to exploit melodic or harmonic characteristics of songs and yield promising performances. In this work, we propose a comparative study of these different representations and show that systems combining melodic and harmonic features drastically outperform those relying on a single input representation. We illustrate how these features complement each other with both quantitative and qualitative analyses. We finally investigate various fusion schemes and propose methods yielding state-of-the-art performances on two publicly-available large datasets., FY is supported by the MIP-Frontiers project, the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 765068, and EG by TROMPA, the Horizon 2020 project 770376-2.},
added-at = {2021-06-03T14:20:35.000+0200},
author = {Doras, Guillaume and Yesiler, Furkan and Serrà Julià, Joan and {Gómez Gutiérrez, Emilia}, 1975 and Peeters, Geoffroy},
biburl = {https://www.bibsonomy.org/bibtex/204dc390a8cccaa561fba81fa593c9a40/simonha94},
description = {Combining musical features for cover detection},
id = {http://hdl.handle.net/10230/45719},
interhash = {d38c1f65f51cfaa36d128f546f845495},
intrahash = {04dc390a8cccaa561fba81fa593c9a40},
keywords = {cover coverdetection detection feature information mir music retrieval},
publisher = {International Society for Music Information Retrieval (ISMIR)},
timestamp = {2021-06-03T14:20:35.000+0200},
title = {Combining musical features for cover detection},
type = {info:eu-repo/semantics/conferenceObject, info:eu-repo/semantics/publishedVersion},
url = {https://repositori.upf.edu/handle/10230/45719},
year = 2020
}