Robust handcrafted features for music genre classification
Author:
Funder
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-022-08069-5.pdf
Reference46 articles.
1. Li S, Karatzoglou A, Gentile C (2016) Collaborative filtering Bandits. In: Proceedings of the 39th international ACM SIGIR conference on research and development in information retrieval. SIGIR ’16, pp 539–548
2. Tzanetakis G, Cook P (2002) Musical genre classification of audio signals. IEEE Trans Speech Audio Process 10(5):293–302. https://doi.org/10.1109/TSA.2002.800560
3. Markov K, Matsui T (2014) Music genre and emotion recognition using Gaussian processes. IEEE Access 2:688–697. https://doi.org/10.1109/ACCESS.2014.2333095
4. Baniya BK, Lee J (2016) Importance of audio feature reduction in automatic music genre classification. Multimedia Tools Appl 75(6):3013–3026. https://doi.org/10.1007/s11042-014-2418-z
5. Foleis JH, Tavares TF (2020) Texture selection for automatic music genre classification. Appl Soft Comput 89:106127. https://doi.org/10.1016/j.asoc.2020.106127
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