Acknowledgement
This research was supported by the Chung-Ang University research grant in 2020. This research was also supported by Next-Generation Information Computing Development Program through the National Research Foundation (NRF) of Korea and the NRF grant funded by the Ministry of Science, ICT (NRF-2017M3C4A7083281, NRF-2021R1F1A1056516).
References
- Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, and Kudlur M (2016). Tensorflow: A system for large-scale machine learning. In Proceedings of 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI '16), Savannah, GA, USA, 265-283.
- Bucilua C, Caruana R, and Niculescu-Mizil A (2006). Model compression. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 535-541.
- Deng J, Dong W, Socher R, Li LJ, Li K, and Fei-Fei L (2009). Imagenet: A large-scale hierarchical image database. In Proceedigns of 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 248-255.
- Edunov S, Ott M, Auli M, and Grangier D (2018). Understanding back-translation at scale, Available from: arXiv preprint arXiv:1808.09381
- Furlanello T, Lipton ZC, Tschannen M, Itti L, and Anandkumar A (2018). Born again neural networks, Available from: arXiv preprint arXiv:1805.04770
- Han S, Mao H, and Dally WJ (2015). Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding, In Proceedings of 4th International Conference on Learning Representations (ICLR 2016), San Juan, Puerto Rico, Available from: arXiv preprint arXiv:1510.00149
- He K, Zhang X, Ren S, and Sun J (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 770-778.
- Hinton G, Vinyals O, and Dean J (2015). Distilling the knowledge in a neural network, Available from: arXiv preprint arXiv:1503.02531
- Krizhevsky A and Hinton G (2009). Learning multiple layers of features from tiny images (Technical report), University of Toronto, 1, 7, Available from: https://www.cs.toronto.edu/kriz/learning-features-2009-TR.pdf
- LeCun Y, Bottou L, Bengio Y, and Haffner P (1998). Gradient-based learning applied to document recognition, Proceedings of the IEEE, 86, 2278-2324. https://doi.org/10.1109/5.726791
- Logan B (2000). Mel frequency cepstral coefficients for music modeling, ISMIR, 270, 1-11.
- McFee B, Raffel C, Liang D, Ellis D, McVicar M, Battenberg E, and Nieto O (2015). Librosa: Audio and Music Signal Analysis in Python. In Proceedings of the 14th Python in Science Conference, Austin, Texas, USA, 18-24.
- Muller M and Ewert S (2011) Chroma toolbox: MATLAB implementations for extracting variants of chroma-based audio features. In Proceedings of the International Conference on Music Information Retrieval, Miami, Florida, USA, 215-220.
- Nesterov YE (1983). A method for solving the convex programming problem with convergence rate O(1/k2), Doklady Akademii Nauk SSSR, 269, 543-547.
- Park W, Kim D, Lu Y, and Cho M (2019). Relational knowledge distillation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 3967-3976.
- Piczak KJ (2015). Environmental sound classification with convolutional neural networks. In Proceedings of 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing, Boston, MA, USA, 1-6.
- Salamon J, Jacoby C, and Bello JP (2014). A dataset and taxonomy for urban sound research. In Proceedings of the 22nd ACM International Conference on Multimedia, Orlando, Florida, USA, 1041-1044.
- Saon G, Kurata G, Sercu T et al. (2017). English conversational telephone speech recognition by humans and machines, In Proceedings of 18th Annual Conference of the International Speech Communication Association, Stockholm, Sweden, 132-136, Available from: arXiv preprint arXiv:1703.02136
- Shepard R (1964). Circularity in judgments of relative pitch, The Journal of the Acoustical Society of America, 36, 2346-2353. https://doi.org/10.1121/1.1919362
- Tan M and Le QV (2019). EfficientNet: Rethinking model scaling for convolutional neural networks, In Proceedings of 36th International Conference on Machine Learning, Long Beach, CA, USA, 6105-6114, Avalilable from: arXiv preprint arXiv:1905.11946
- Zhang Z, Xing F, Su H, Shi X, and Yang L (2017). Recent advances in the applications of convolutional neural networks to medical image contour detection, Available from: https://doi.org/10.48550/arXiv.1708.07281