Abstract
In recent years, convolutional neural networks (CNNs) become popular approaches used in music information retrieval (MIR) tasks, such as mood recognition, music auto-tagging and so on. Since CNNs are able to extract the local features effectively, previous attempts show great performance on music auto-tagging. However, CNNs is not able to capture the spatial features and the relationship between low-level features are neglected. Motivated by this problem, a hybrid architecture is proposed based on Capsule Network, which is capable to extract spatial features with the routing-by-agreement mechanism. The proposed model was applied in music auto-tagging. The results show that it achieves promising results of the ROC-AUC score of 90.67%.
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References
Nanni, L., Costa, Y.M.G., Lumini, A., et al.: Combining visual and acoustic features for music genre classification. Expert Syst. Appl. 45, 108–117 (2016)
Lee, J., Park, J., Kim, K.L., et al.: Sample-level deep convolutional neural networks for music auto-tagging using raw waveforms. arXiv preprint arXiv:1703.01789 (2017)
Su, L.: Vocal melody extraction using patch-based CNN. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 371–375. IEEE (2018)
Lee, J., Nam, J.: Multi-level and multi-scale feature aggregation using pretrained convolutional neural networks for music auto-tagging. IEEE Signal Process. Lett. 24(8), 1208–1212 (2017)
Choi, K., Fazekas, G., Sandler, M., et al.: Convolutional recurrent neural networks for music classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2392–2396. IEEE (2017)
Pons, J., Nieto, O., Prockup, M., et al.: End-to-end learning for music audio tagging at scale. arXiv preprint arXiv:1711.02520 (2017)
Wu, X., Liu, S., Cao, Y., et al.: Speech emotion recognition using capsule networks. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6695–6699. IEEE (2019)
Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: Advances in Neural Information Processing Systems, pp. 3856–3866 (2017)
Choi, K., Fazekas, G., Sandler, M.: Automatic tagging using deep convolutional neural networks. arXiv preprint arXiv:1606.00298 (2016)
Law, E., West, K., Mandel, M.I., et al.: Evaluation of algorithms using games: the case of music tagging. In: ISMIR, pp. 387–392 (2009)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
Srivastava, N., Hinton, G., Krizhevsky, A., et al.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
McFee, B., Raffel, C., Liang, D., et al.: librosa: audio and music signal analysis in python. In: Proceedings of the 14th Python in Science Conference, vol. 8 (2015)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_1
Nam, J., Herrera, J., Lee, K.: A deep bag-of-features model for music auto-tagging. arXiv preprint arXiv:1508.04999 (2015)
Dieleman, S., Schrauwen, B.: End-to-end learning for music audio. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6964–6968. IEEE (2014)
Van Den Oord, A., Dieleman, S., Schrauwen, B.: Transfer learning by supervised pre-training for audio-based music classification. In: Conference of the International Society for Music Information Retrieval (ISMIR 2014) (2014)
Acknowledgement
This work was supported by Research Fund for Sichuan Science and Technology Program (GrantNo. 2019YFG0190) and Research on Sino-Tibetan multisource information acquisition, fusion, data mining and its application (Grant No. H04W170186).
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Yu, Y., Tang, Y., Qi, M., Mai, F., Deng, Q., Nima, Z. (2020). Music Auto-Tagging with Capsule Network. In: Zeng, J., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2020. Communications in Computer and Information Science, vol 1257. Springer, Singapore. https://doi.org/10.1007/978-981-15-7981-3_20
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DOI: https://doi.org/10.1007/978-981-15-7981-3_20
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