Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Gabriel Meseguer Brocal"'
Publikováno v:
Transactions of the International Society for Music Information Retrieval, Vol 3, Iss 1 (2020)
The DALI dataset is a large dataset of time-aligned symbolic vocal melody notations (notes) and lyrics at four levels of granularity. DALI contains 5358 songs in its first version and 7756 for the second one. In this article, we present the dataset,
Externí odkaz:
https://doaj.org/article/17d42789cf474c1499b2e0f2799bd90a
Publikováno v:
MOCO '22: Proceedings of the 8th International Conference on Movement and Computing
MOCO '22: 8th International Conference on Movement and Computing
MOCO '22: 8th International Conference on Movement and Computing, Jul 2022, Chicago, France. pp.1-13, ⟨10.1145/3537972.3537998⟩
MOCO '22: 8th International Conference on Movement and Computing
MOCO '22: 8th International Conference on Movement and Computing, Jul 2022, Chicago, France. pp.1-13, ⟨10.1145/3537972.3537998⟩
International audience; Human movements support communication, and can be used to imitate actions or physical phenomenons. Observing gestural imitations of short sounds, we found that such gestures can be categorized by their frequency content. To an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c78b48af299cab64170a1b7e80abbc43
https://hal.science/hal-03711293/document
https://hal.science/hal-03711293/document
Autor:
Rachel M. Bittner, Juan Jose Bosch, David Rubinstein, Gabriel Meseguer-Brocal, Sebastian Ewert
Automatic Music Transcription (AMT) has been recognized as a key enabling technology with a wide range of applications. Given the task's complexity, best results have typically been reported for systems focusing on specific settings, e.g. instrument-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::218adb4991d71586eec9b48c8e102551
Autor:
Elena Cabrio, Fabien Gandon, Yaroslav Nechaev, Michael Fell, Geoffroy Peeters, Gabriel Meseguer-Brocal
Publikováno v:
Natural Language Engineering
Natural Language Engineering, 2021, 28 (3), pp.1-20. ⟨10.1017/S1351324921000024⟩
Natural Language Engineering, Cambridge University Press (CUP), 2021, pp.1-20. ⟨10.1017/S1351324921000024⟩
Natural Language Engineering, 2021, 28 (3), pp.1-20. ⟨10.1017/S1351324921000024⟩
Natural Language Engineering, Cambridge University Press (CUP), 2021, pp.1-20. ⟨10.1017/S1351324921000024⟩
Song lyrics contain repeated patterns that have been proven to facilitate automated lyrics segmentation, with the final goal of detecting the building blocks (e.g., chorus, verse) of a song text. Our contribution in this article is twofold. First, we
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::09ca9426e7ca0121dfc8e27d50eda1ad
https://hal.science/hal-03295581
https://hal.science/hal-03295581
Publikováno v:
Proceedings of the 21st International Society for Music Information Retrieval Conference
21st International Society for Music Information Retrieval Conference
21st International Society for Music Information Retrieval Conference, Oct 2020, Montréal (virtual), Canada
HAL
21st International Society for Music Information Retrieval Conference
21st International Society for Music Information Retrieval Conference, Oct 2020, Montréal (virtual), Canada
HAL
Informed source separation has recently gained renewed interest with the introduction of neural networks and the availability of large multitrack datasets containing both the mixture and the separated sources. These approaches use prior information a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7161b1b6007d9709cade7779ecde3bad
https://hal.archives-ouvertes.fr/hal-03200161/document
https://hal.archives-ouvertes.fr/hal-03200161/document
Autor:
Gabriel Meseguer Brocal
Publikováno v:
Sound [cs.SD]. Sorbonne Université, 2020. English
Sound [cs.SD]. Sorbonne Université, 2020. English. ⟨NNT : 2020SORUS111⟩
Gabriel Meseguer Brocal
HAL
Sound [cs.SD]. Sorbonne Université, 2020. English. ⟨NNT : 2020SORUS111⟩
Gabriel Meseguer Brocal
HAL
This dissertation proposes the study of multimodal learning in the context of musical signals. Throughout, we focus on the interaction between audio signals and text information. Among the many text sources related to music that can be used (e.g. rev
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d36ebc0874f9bad832eb66312e5e9303
https://hal.archives-ouvertes.fr/tel-03200189
https://hal.archives-ouvertes.fr/tel-03200189
Publikováno v:
Proceedings of the 19th ISMIR Conference
19th International Society for Music Information Retrieval Conference
19th International Society for Music Information Retrieval Conference, Sep 2018, Paris, France
HAL
19th International Society for Music Information Retrieval Conference
19th International Society for Music Information Retrieval Conference, Sep 2018, Paris, France
HAL
International audience; The goal of this paper is twofold. First, we introduce DALI, a large and rich multimodal dataset containing 5358 audio tracks with their time-aligned vocal melody notes and lyrics at four levels of granularity. The second goal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::97370cbe9a6bba6da005e723900f03f2
Autor:
Gabriel Meseguer Brocal, Geoffroy Peeters, Guillaume Pellerin, Michel Buffa, Elena Cabrio, Catherine Faron Zucker, Alain Giboin, Isabelle Mirbel, Romain Hennequin, Manuel Moussallam, Francesco Piccoli, Thomas Fillon
Publikováno v:
Proceedings of 3rd Web Audio Conference, London, 2017
Web Audio Conference 2017 – Collaborative Audio #WAC2017
Web Audio Conference 2017 – Collaborative Audio #WAC2017, Queen Mary University of London, Aug 2017, London, United Kingdom
HAL
Web Audio Conference 2017 – Collaborative Audio #WAC2017
Web Audio Conference 2017 – Collaborative Audio #WAC2017, Queen Mary University of London, Aug 2017, London, United Kingdom
HAL
International audience; This paper presents the WASABI project, started in 2017, which aims at (1) the construction of a 2 million song knowledge base that combines metadata collected from music databases on the Web, metadata resulting from the analy
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::29b214851e95a13933cb69ee67a5ed4b
https://hal.univ-cotedazur.fr/hal-01589250/document
https://hal.univ-cotedazur.fr/hal-01589250/document
The goal of this paper is twofold. First, we introduce DALI, a large and rich multimodal dataset containing 5358 audio tracks with their time-aligned vocal melody notes and lyrics at four levels of granularity. The second goal is to explain our metho
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::12ee37a22d461ffe3f2a8ffdfb8d65c7
Data-driven models for audio source separation such as U-Net or Wave-U-Net are usually models dedicated to and specifically trained for a single task, e.g. a particular instrument isolation. Training them for various tasks at once commonly results in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ec5626fb115b8e1c9891965fffafdc5e