Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Yesiler, Furkan"'
Autor:
Parker, Julian D., Spijkervet, Janne, Kosta, Katerina, Yesiler, Furkan, Kuznetsov, Boris, Wang, Ju-Chiang, Avent, Matt, Chen, Jitong, Le, Duc
End-to-end generation of musical audio using deep learning techniques has seen an explosion of activity recently. However, most models concentrate on generating fully mixed music in response to abstract conditioning information. In this work, we pres
Externí odkaz:
http://arxiv.org/abs/2312.08723
Version identification (VI) systems now offer accurate and scalable solutions for detecting different renditions of a musical composition, allowing the use of these systems in industrial applications and throughout the wider music ecosystem. Such use
Externí odkaz:
http://arxiv.org/abs/2109.15188
In this article, we aim to provide a review of the key ideas and approaches proposed in 20 years of scientific literature around musical version identification (VI) research and connect them to current practice. For more than a decade, VI systems suf
Externí odkaz:
http://arxiv.org/abs/2109.02472
The setlist identification (SLI) task addresses a music recognition use case where the goal is to retrieve the metadata and timestamps for all the tracks played in live music events. Due to various musical and non-musical changes in live performances
Externí odkaz:
http://arxiv.org/abs/2101.02098
Version identification systems aim to detect different renditions of the same underlying musical composition (loosely called cover songs). By learning to encode entire recordings into plain vector embeddings, recent systems have made significant prog
Externí odkaz:
http://arxiv.org/abs/2010.03284
The version identification (VI) task deals with the automatic detection of recordings that correspond to the same underlying musical piece. Despite many efforts, VI is still an open problem, with much room for improvement, specially with regard to co
Externí odkaz:
http://arxiv.org/abs/1910.12551
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 perspecti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cc0fd863ba0d71dfb7f838960c7381ae
http://hdl.handle.net/10230/45719
http://hdl.handle.net/10230/45719
Autor:
Yesiler, Furkan
Analysis of expression in singing voice is gaining more importance as the current assessment systems fail to consider important resources in expressive singing, e.g. phonation modes. Phonation modes have been divided into four categories (breathy, pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b2cf75b164c134cf020d846750f5f49d
Publikováno v:
Recercat. Dipósit de la Recerca de Catalunya
instname
Scopus-Elsevier
instname
Scopus-Elsevier
Comunicació presentada a: 15th Sound and Music Computing Conference (SMC2018). Sonic crossing, celebrat a Limassol, Xipre, del 4 al 7 de juliol de 2018. This work focuses on automatic makam recognition taskfor Turkish makam music (TMM) using pitch d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5b61d9e751bfc3c67c26b869fa0e7d6d