References
- R. Sarkar, N. Biswas, and S. Chakraborty, "Music genre classification using frequency domain features,' in Proceedings of 2018 5th International Conference on Emerging Applications of Information Technology (EAIT), Kolkata, India, 2018, pp. 1-4. https://doi.org/10.1109/EAIT.2018.8470441
- C. Weiss, F. Brand, and M. Muller, "Mid-level chord transition features for musical style analysis," in Proceedings of 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, 2019, pp. 341-345. https://doi.org/10.1109/ICASSP.2019.8682293
- J. H. Foleis and T. F. Tavares, "Texture selection for automatic music genre classification," Applied Soft Computing, vol. 89, article no. 106127, 2020. https://doi.org/10.1016/j.asoc.2020.106127
- A. Vidwans, P. Verma, and P. Rao, "Classifying cultural music using melodic features," in Proceedings of 2020 International Conference on Signal Processing and Communications (SPCOM), Bangalore, India, 2020, pp. 1-5. https://doi.org/10.1109/SPCOM50965.2020.9179597
- L. K. Puppala, S. S. R. Muvva, S. R. Chinige, and P. S. Rajendran, "A novel music genre classification using convolutional neural network," in Proceedings of 2021 6th International Conference on Communication and Electronics Systems (ICCES), Coimbatre, India, 2021, pp. 1246-1249. https://doi.org/10.1109/ICCES51350.2021.9489022
- C. Chen and X. Steven, "Combined transfer and active learning for high accuracy music genre classification method," in Proceedings of 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), Nanchang, China, 2021, pp. 53-56. https://doi.org/10.1109/ICBAIE52039.2021.9390062
- J. L. Conceicao, R. de Freitas, B. Gadelha, J. G. Kienen, S. Anders, and B. Cavalcante, "Applying supervised learning techniques to Brazilian music genre classification," in Proceedings of 2020 XLVI Latin American Computing Conference (CLEI), Loja, Ecuador, 2020, pp. 102-107. https://doi.org/10.1109/CLEI52000.2020.00019
- N. Pelchat and C. M. Gelowitz, "Neural network music genre classification" Canadian Journal of Electrical and Computer Engineering, vol. 43, no. 3, pp. 170-173, 2020. https://doi.org/10.1109/CJECE.2020.2970144
- J. S. Luz, M. C. Oliveira, F. H. Araujo, and D. M. Magalhaes, "Ensemble of handcrafted and deep features for urban sound classification," Applied Acoustics, vol. 175, article no. 107819, 2021. https://doi.org/10.1016/j.apacoust.2020.107819
- D. Taufik and N. Hanafiah, "AutoVAT: an automated visual acuity test using spoken digit recognition with MEL frequency cepstral coefficients and convolutional neural network," Procedia Computer Science, vol. 179, pp. 458-467, 2021. https://doi.org/10.1016/j.procs.2021.01.029
- S. Wang, H. Wang, Q. Gao, and L. Hao, "Auto-encoder neural network based prediction of Texas poker opponent's behavior," Entertainment Computing, vol. 40, article no. 100446, 2022. https://doi.org/10.1016/j.entcom.2021.100446
- M. Seo and K. Y. Lee, "A graph embedding technique for weighted graphs based on LSTM autoencoders," Journal of Information Processing Systems, vol. 16, no. 6, pp. 1407-1423, 2020. https://doi.org/10.3745/JIPS.04.0197
- B. M. Aslahi-Shahri, R. Rahmani, M. Chizari, A. Maralani, M. Eslami, M. J. Golkar, and A. Ebrahimi, "A hybrid method consisting of GA and SVM for intrusion detection system," Neural Computing and Applications, vol. 27, pp. 1669-1676, 2016. https://doi.org/10.1007/s00521-015-1964-2
- A. C. Enache and V. Sgarciu, "Anomaly intrusions detection based on support vector machines with an improved bat algorithm," in Proceedings of 2015 20th International Conference on Control Systems and Computer Science, Bucharest, Romania, 2015, pp. 317-321. https://doi.org/10.1109/CSCS.2015.12
- Y. Chen and R. Zhang, "Default prediction of automobile credit based on support vector machine," Journal of Information Processing Systems, vol. 17, no. 1, pp. 75-88, 2021. https://doi.org/10.3745/JIPS.04.0207
- H. Dai, J. Li, Y. Kuang, J. Liao, Q. Zhang, and Y. Kang, "Multiscale fuzzy entropy and PSO-SVM based fault diagnoses for airborne fuel pumps," Human-centric Computing and Information Sciences, vol. 11, article no. 25, 2021. https://doi.org/10.22967/HCIS.2021.11.025
- K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," 2015 [Online]. Available: https://arxiv.org/abs/1512.03385.
- Andrada, "GTZAN Dataset - Music Genre Classification," 2022 [Online]. Available: https://www.kaggle.com/andradaolteanu/gtzan-dataset-music-genre-classification.
- Y. Singh and A. Biswas, "Robustness of musical features on deep learning models for music genre classification," Expert Systems with Applications, vol. 199, article no. 116879, 2022. https://doi.org/10.1016/j.eswa.2022.116879
- Y. Yu, S. Luo, S. Liu, H. Qiao, Y. Liu, and L. Feng, "Deep attention based music genre classification," Neurocomputing, vol. 372, pp. 84-91, 2020. https://doi.org/10.1016/j.neucom.2019.09.054
- C. Liu, L. Feng, G. Liu, H. Wang, and S. Liu, "Bottom-up broadcast neural network for music genre classification," Multimedia Tools and Applications, vol. 80, pp. 7313-7331, 2021. https://doi.org/10.1007/s11042-020-09643-6