We provide a survey of the field of Music Information Retrieval (MIR), in particular paying attention to latest developments, such as semantic auto-tagging and user-centric retrieval and recommendation approaches. We first elaborate on well-established and proven methods for feature extraction and music indexing, from both the audio signal and contextual data sources about music items, such as web pages or collaborative tags. These in turn enable a wide variety of music retrieval tasks, such as semantic music search or music identification (“query by example”). Subsequently, we review current work on user analysis and modeling in the context of music recommendation and retrieval, addressing the recent trend towards user-centric and adaptive approaches and systems. A discussion follows about the important aspect of how various MIR approaches to different problems are evaluated and compared. Eventually, a discussion about the major open challenges concludes the survey.
X. Du, Z. Yu, B. Zhu, X. Chen, and Z. Ma. Proceedings of the International Conference on Acoustics, Speech and Signal Processing, page 551--555. IEEE, (June 2021)
A. Vaglio, R. Hennequin, M. Moussallam, and G. Richard. Proceedings of the 22nd International Society for Music Information Retrieval Conferenc, Society for Music Information Retrieval, (November 2021)
A. Correya, R. Hennequin, and M. Arcos. (2018)cite arxiv:1808.10351Comment: Music Information Retrieval, Cover Song Identification, Million Song Dataset, Natural Language Processing.
J. Su, T. Hong, J. Li, and J. Su. Proceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018, (December 2018)
A. Correya, R. Hennequin, and M. Arcos. (2018)cite arxiv:1808.10351Comment: Music Information Retrieval, Cover Song Identification, Million Song Dataset, Natural Language Processing.
S. Hachmeier, R. Jäschke, and H. Saadatdoorabi. Proceedings of the Conference on ``Lernen, Wissen, Daten, Analysen'', 3341, page 213--226. Aachen, (2022)