Predicting Music Genres from Audio, Metadata and Text Features
DOI:
https://doi.org/10.54097/hset.v12i.1356Keywords:
Naive Bayes, KNN, Decision Tree, Random Forest, Machine Learning Prediction.Abstract
This paper focuses on identify music genres by using machine learning techniques and predictive models to categorize music without specific criteria based on audio, metadata, and text aspects.
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References
Tao, L., Ogihara, M. and Qi, L., 2003. A Comparative Study on Content-Based Music Genre Classification. SIGIR Forum, pp.282-289.
Tao, Li, and Mitsunori Ogihara. "Music Genre Classification With Taxonomy". Institute Of Electrical And Electronics Engineers Inc., 2020, pp. 197-200, Accessed 15 Oct 2005.
Anders, Meng et al. "Temporal Feature Integration For Music Genre Classification". I E E E Transactions On Audio, Speech And Language Processing, 2007, Accessed 15 Oct 2020.
T. Bertin-Mahieux, D. P.W. Ellis, B. Whitman, and P. Lamere. The million song dataset. In Proceedings of the 12th International Conference on Music Information Retrieval (ISMIR), 2011.
A. Schindler and A. Rauber. Capturing the temporal domain in Echonest Features for improved classification effectiveness. In Proceedings of the 10th International Workshop on Adaptive Multimedia Retrieval (AMR), 2012.
Piryonesi S. Madeh; El-Diraby Tamer E. (2020-06-01). "Role of Data Analytics in Infrastructure Asset Management: Overcoming Data Size and Quality Problems". Journal of Transportation Engineering, Part B: Pavements. 146 (2): 04020022. doi:10.1061/JPEODX.0000175.
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