Relevant bibliographies by topics / Auto-tagging musical

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  1. Journal articles
  2. Dissertations / Theses
  3. Conference papers

Academic literature on the topic 'Auto-tagging musical'

Author: Grafiati
Published: 1 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Auto-tagging musical"

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Oramas, Sergio, Fabien Gouyon, Steve Hogan, Camilo Landau, and Andreas Ehmann. "MGPHot: A Dataset of Musicological Annotations for Popular Music (1958–2022)." Transactions of the International Society for Music Information Retrieval 8, no. 1 (2025). https://doi.org/10.5334/tismir.236.

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The Music Genome Project® is an extensive music annotation effort spanning two decades, during which a team of musicologists has been annotating a dataset of millions of songs with hundreds of musicological attributes. A derivative of this effort is presented in this paper. We are releasing MGPHot, a dataset of more than 21,000 songs that have appeared at least once in the Billboard Hot 100 charts from 1958 until 2022, annotated with 58 musical attributes that are grouped into seven different categories: rhythm, compositional focus, harmony, instrumentation, sonority, vocals, and lyrics. Given
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Dissertations / Theses on the topic "Auto-tagging musical"

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Ibrahim, Karim M. "Personalized audio auto-tagging as proxy for contextual music recommendation." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT039.

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La croissance exponentielle des services en ligne et des données des utilisateurs a changé la façon dont nous interagissons avec divers services, et la façon dont nous explorons et sélectionnons de nouveaux produits. Par conséquent, il existe un besoin croissant de méthodes permettant de recommander les articles appropriés pour chaque utilisateur. Dans le cas de la musique, il est plus important de recommander les bons éléments au bon moment. Il est bien connu que le contexte, c'est-à-dire la situation d'écoute des utilisateurs, influence fortement leurs préférences d'écoute. C'est pourquoi le
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Conference papers on the topic "Auto-tagging musical"

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Silva, Diego Furtado, Angelo Cesar Mendes da Silva, Luís Felipe Ortolan, and Ricardo Marcondes Marcacini. "On Generalist and Domain-Specific Music Classification Models and Their Impacts on Brazilian Music Genre Recognition." In Simpósio Brasileiro de Computação Musical. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/sbcm.2021.19427.

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Deep learning has become the standard procedure to deal with Music Information Retrieval problems. This category of machine learning algorithms has achieved state-of-the-art results in several tasks, such as classification and auto-tagging. However, obtaining a good-performing model requires a significant amount of data. At the same time, most of the music datasets available lack cultural diversity. Therefore, the performance of the currently most used pre-trained models on underrepresented music genres is unknown. If music models follow the same direction that language models in Natural Langu
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Liu, Jen-Yu, and Yi-Hsuan Yang. "Event Localization in Music Auto-tagging." In MM '16: ACM Multimedia Conference. ACM, 2016. http://dx.doi.org/10.1145/2964284.2964292.

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Ibrahim, Karim M., Jimena Royo-Letelier, Elena V. Epure, Geoffroy Peeters, and Gael Richard. "Audio-Based Auto-Tagging With Contextual Tags for Music." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054352.

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Yang, Yi-Hsuan. "Towards real-time music auto-tagging using sparse features." In 2013 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2013. http://dx.doi.org/10.1109/icme.2013.6607505.

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Lin, Yi-Hsun, Chia-Hao Chung, and Homer H. Chen. "Playlist-Based Tag Propagation for Improving Music Auto-Tagging." In 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018. http://dx.doi.org/10.23919/eusipco.2018.8553318.

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Yeh, Chin-Chia Michael, Ju-Chiang Wang, Yi-Hsuan Yang, and Hsin-Min Wang. "Improving music auto-tagging by intra-song instance bagging." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6853977.

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Yan, Qin, Cong Ding, Jingjing Yin, and Yong Lv. "Improving music auto-tagging with trigger-based context model." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178006.

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Joung, Haesun, and Kyogu Lee. "Music Auto-Tagging with Robust Music Representation Learned via Domain Adversarial Training." In ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024. http://dx.doi.org/10.1109/icassp48485.2024.10447318.

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Yin, Jingjing, Qin Yan, Yong Lv, and Qiuyu Tao. "Music auto-tagging with variable feature sets and probabilistic annotation." In 2014 9th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP). IEEE, 2014. http://dx.doi.org/10.1109/csndsp.2014.6923816.

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Wang, Shuo-Yang, Ju-Chiang Wang, Yi-Hsuan Yang, and Hsin-Min Wang. "Towards time-varying music auto-tagging based on CAL500 expansion." In 2014 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2014. http://dx.doi.org/10.1109/icme.2014.6890290.

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