Relevant bibliographies by topics / Music auto-tagging

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Music auto-tagging'

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

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Journal articles on the topic "Music auto-tagging"

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Akama, Taketo, Hiroaki Kitano, Katsuhiro Takematsu, Yasushi Miyajima, and Natalia Polouliakh. "Auxiliary self-supervision to metric learning for music similarity-based retrieval and auto-tagging." PLOS ONE 18, no. 11 (2023): e0294643. http://dx.doi.org/10.1371/journal.pone.0294643.

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In the realm of music information retrieval, similarity-based retrieval and auto-tagging serve as essential components. Similarity-based retrieval involves automatically analyzing a music track and fetching analogous tracks from a database. Auto-tagging, on the other hand, assesses a music track to deduce associated tags, such as genre and mood. Given the limitations and non-scalability of human supervision signals, it becomes crucial for models to learn from alternative sources to enhance their performance. Contrastive learning-based self-supervised learning, which exclusively relies on learn
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Bengani, Shaleen, S. Vadivel, and J. Angel Arul Jothi. "Efficient Music Auto-Tagging with Convolutional Neural Networks." Journal of Computer Science 15, no. 8 (2019): 1203–8. http://dx.doi.org/10.3844/jcssp.2019.1203.1208.

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Song, Guangxiao, Zhijie Wang, Fang Han, Shenyi Ding, and Muhammad Ather Iqbal. "Music auto-tagging using deep Recurrent Neural Networks." Neurocomputing 292 (May 2018): 104–10. http://dx.doi.org/10.1016/j.neucom.2018.02.076.

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Lee, Jaehwan, Daekyeong Moon, Jik-Soo Kim, and Minkyoung Cho. "ATOSE: Audio Tagging with One-Sided Joint Embedding." Applied Sciences 13, no. 15 (2023): 9002. http://dx.doi.org/10.3390/app13159002.

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Audio auto-tagging is the process of assigning labels to audio clips for better categorization and management of audio file databases. With the advent of advanced artificial intelligence technologies, there has been increasing interest in directly using raw audio data as input for deep learning models in order to perform tagging and eliminate the need for preprocessing. Unfortunately, most current studies of audio auto-tagging cannot effectively reflect the semantic relationships between tags—for instance, the connection between “classical music” and “cello”. In this paper, we propose a novel
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Ellis, Katherine, Emanuele Coviello, Antoni B. Chan, and Gert Lanckriet. "A Bag of Systems Representation for Music Auto-Tagging." IEEE Transactions on Audio, Speech, and Language Processing 21, no. 12 (2013): 2554–69. http://dx.doi.org/10.1109/tasl.2013.2279318.

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Shao, Xi, Zhiyong Cheng, and Mohan S. Kankanhalli. "Music auto-tagging based on the unified latent semantic modeling." Multimedia Tools and Applications 78, no. 1 (2018): 161–76. http://dx.doi.org/10.1007/s11042-018-5632-2.

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Song, Guangxiao, Zhijie Wang, Fang Han, Shenyi Ding, and Xiaochun Gu. "Music auto-tagging using scattering transform and convolutional neural network with self-attention." Applied Soft Computing 96 (November 2020): 106702. http://dx.doi.org/10.1016/j.asoc.2020.106702.

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Lee, Jongpil, and Juhan Nam. "Multi-Level and Multi-Scale Feature Aggregation Using Pretrained Convolutional Neural Networks for Music Auto-Tagging." IEEE Signal Processing Letters 24, no. 8 (2017): 1208–12. http://dx.doi.org/10.1109/lsp.2017.2713830.

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Yu, Yong-bin, Min-hui Qi, Yi-fan Tang, Quan-xin Deng, Feng Mai, and Nima Zhaxi. "A sample-level DCNN for music auto-tagging." Multimedia Tools and Applications, January 6, 2021. http://dx.doi.org/10.1007/s11042-020-10330-9.

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Lin, Yi-Hsun, and Homer Chen. "Tag Propagation and Cost-Sensitive Learning for Music Auto-Tagging." IEEE Transactions on Multimedia, 2020, 1. http://dx.doi.org/10.1109/tmm.2020.3001521.

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Dissertations / Theses on the topic "Music auto-tagging"

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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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Semela, René. "Automatické tagování hudebních děl pomocí metod strojového učení." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-413253.

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One of the many challenges of machine learning are systems for automatic tagging of music, the complexity of this issue in particular. These systems can be practically used in the content analysis of music or the sorting of music libraries. This thesis deals with the design, training, testing, and evaluation of artificial neural network architectures for automatic tagging of music. In the beginning, attention is paid to the setting of the theoretical foundation of this field. In the practical part of this thesis, 8 architectures of neural networks are designed (4 fully convolutional and 4 conv
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Chiang, Yen-Lin, and 江衍霖. "Sketch-based Music Retrieval Based on Frame-level Auto-tagging Predictions." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/687mkw.

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碩士<br>國立清華大學<br>資訊工程學系所<br>105<br>We proposed a novel and intuitive music retrieval interface that allows users to precisely search music containing multiple localized social tags with merely simple sketches. For example, one may search for a “classical” music clip that also includes a segment with “violin”, followed by another segment which simultaneously includes “slow” and “guitar”, while such complex conditions can be simply and correctly expressed in the query. We also proposed a segment-level database with thousands of songs and its preprocessing algorithms for our music retrieval method
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Sheng-WeiSyu and 徐陞瑋. "A Method of Music Auto-tagging Based on Audio and Lyric." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/9e8723.

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碩士<br>國立成功大學<br>資訊管理研究所<br>107<br>With the development of the Internet and technology, online music platforms and music streaming services are booming, the large number of digital music makes users face the problem of information overloading. In order to solve this problem, these platforms need to construct a comprehensive recommendation system by using user information and meta data to help users in searching, querying or discovering new music. Social tags are considered to help the music recommendation system to make better recommendations. However, social tags face the problem of tag sparsi
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Yang, Jia-Hong, and 楊佳虹. "A Robust Music Auto-Tagging Technique Using Audio Fingerprinting and Deep Convolutional Neural Networks." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/vagbse.

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碩士<br>國立中興大學<br>資訊科學與工程學系<br>106<br>Music tags are a set of descriptive keywords that convey high-level information about a music clip, such as emotions(sadness, happiness), genres(jazz, classical), and instruments(guitar, vocal). Since tags provide high-level information from the listener’s perspectives, they can be used for music discovery and recommendation. However, in music information retrieval (MIR), researchers need to have expertise based on acoustics or engineering design in order to analyze and organize music informations, classify them according to music forms, and then provide mus
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Arjannikov, Tom. "Positive unlabeled learning applications in music and healthcare." Thesis, 2021. http://hdl.handle.net/1828/13376.

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The supervised and semi-supervised machine learning paradigms hinge on the idea that the training data is labeled. The label quality is often brought into question, and problems related to noisy, inaccurate, or missing labels are studied. One of these is an interesting and prevalent problem in the semi-supervised classification area where only some positive labels are known. At the same time, the remaining and often the majority of the available data is unlabeled, i.e., there are no negative examples. Known as Positive-Unlabeled (PU) learning, this problem has been identified with increasing f
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Book chapters on the topic "Music auto-tagging"

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Yu, Yongbin, Yifan Tang, Minhui Qi, Feng Mai, Quanxin Deng, and Zhaxi Nima. "Music Auto-Tagging with Capsule Network." In Communications in Computer and Information Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7981-3_20.

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Dabral, Tanmaya Shekhar, Amala Sanjay Deshmukh, and Aruna Malapati. "A Multi-scale Convolutional Neural Network Architecture for Music Auto-Tagging." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1592-3_60.

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Ju, Chen, Lixin Han, and Guozheng Peng. "Music Auto-tagging Based on Attention Mechanism and Multi-label Classification." In Lecture Notes in Electrical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6963-7_23.

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Nguyen Cao Minh, Khanh, Thinh Dang An, Vu Tran Quang, and Van Hoai Tran. "Comparative Study on Different Approaches in Optimizing Threshold for Music Auto-Tagging." In Future Data and Security Engineering. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03192-3_18.

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Dubnov Shlomo and Kiyoki Yasushi. "Opera of Meaning: film and music performance with semantic associative search." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2009. https://doi.org/10.3233/978-1-58603-957-8-384.

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Recently artists are exploring ways for incorporating large amounts of information and networking as part of their medium. One of the main challenges in applying information technology to film and opera is in relating different types of media to the meaning of story narrative. Opera of Meaning is a new format for distributed, collaborative and interactive viewing where the association of different media elements is done dynamically by semantic and impression search that is performed by the public during the performance in context of a main story. This opens new research questions in database m
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Conference papers on the topic "Music auto-tagging"

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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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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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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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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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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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