%0 Conference Paper %1 vaglio2021words %A Vaglio, Andrea %A Hennequin, Romain %A Moussallam, Manuel %A Richard, Gael %B Proceedings of the 22nd International Society for Music Information Retrieval Conferenc %D 2021 %K audio cover duplicate identification lyrics mir music similarity uncovr %T The Words Remain the Same: Cover Detection with Lyrics Transcription %U https://hal.archives-ouvertes.fr/hal-03356164/ %X Cover detection has gained sustained interest in the scientific community and has recently made significant progress both in terms of scalability and accuracy. However, most approaches are based on the estimation of harmonic and melodic features and neglect lyrics information although it is an important invariant across covers. In this work, we propose a novel approach leveraging lyrics without requiring access to full texts though the use of lyrics recognition on audio. Our approach relies on the fusion of a singing voice recognition framework and a more classic tonal-based cover detection method. To the best of our knowledge, this is the first time that lyrics estimation from audio has been explicitly used for cover detection. Furthermore, we exploit efficient string matching and an approximated nearest neighbors search algorithm which lead to a scalable system which is able to operate on very large databases. Extensive experiments on the largest publicly available cover detection dataset demonstrate the validity of using lyrics information for this task.

@inproceedings{vaglio2021words, abstract = {Cover detection has gained sustained interest in the scientific community and has recently made significant progress both in terms of scalability and accuracy. However, most approaches are based on the estimation of harmonic and melodic features and neglect lyrics information although it is an important invariant across covers. In this work, we propose a novel approach leveraging lyrics without requiring access to full texts though the use of lyrics recognition on audio. Our approach relies on the fusion of a singing voice recognition framework and a more classic tonal-based cover detection method. To the best of our knowledge, this is the first time that lyrics estimation from audio has been explicitly used for cover detection. Furthermore, we exploit efficient string matching and an approximated nearest neighbors search algorithm which lead to a scalable system which is able to operate on very large databases. Extensive experiments on the largest publicly available cover detection dataset demonstrate the validity of using lyrics information for this task.}, added-at = {2021-10-15T08:18:13.000+0200}, author = {Vaglio, Andrea and Hennequin, Romain and Moussallam, Manuel and Richard, Gael}, biburl = {https://www.bibsonomy.org/bibtex/2060d34b050dbff613b05a2ee1a7241a8/jaeschke}, booktitle = {Proceedings of the 22nd International Society for Music Information Retrieval Conferenc}, id = {https://hal.telecom-paris.fr/hal-03356164, hal-03356164, https://hal.telecom-paris.fr/hal-03356164/document}, interhash = {84fe575b8b5b21d15ff891df34231d7f}, intrahash = {060d34b050dbff613b05a2ee1a7241a8}, keywords = {audio cover duplicate identification lyrics mir music similarity uncovr}, month = nov, organization = {Society for Music Information Retrieval}, timestamp = {2021-10-15T08:18:13.000+0200}, title = {The Words Remain the Same: Cover Detection with Lyrics Transcription}, url = {https://hal.archives-ouvertes.fr/hal-03356164/}, year = 2021 }