Music Auto-tagging Based on Attention Mechanism and Multi-label Classification
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-16-6963-7_23
Reference21 articles.
1. Shaleen, B., Vadivel, S., Arul, J.J.: Efficient music auto-tagging with convolutional neural networks. J. Comput. Sci. 15(8), 1203–1208 (2019)
2. Jiao, P., Yang, Y.: Music annotation and retrieval using unlabeled exemplars: correlation and sparse codes. IEEE Signal Process. Lett. 22(10), 1771–1775 (2015)
3. 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)
4. Jialien, H., ChienChang, H.: Designing a graph-based framework to support a multi-modal approach for music information retrieval. Multimedia Tools Appl. 74(15), 5401–5427 (2015)
5. Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: 2014 European Conference on Computer Vision, pp. 818–833. Springer, Berlin (2014)
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