2017

A Comparison of Audio Signal Preprocessing Methods for Deep Neural Networks on Music Tagging

Choi, Keunwoo, Fazekas, György, Cho, Kyunghyun et al.

Understand

In this paper, we empirically investigate the effect of audio preprocessing on music tagging with deep neural networks.

We perform comprehensive experiments involving audio preprocessing using different time-frequency representations, logarithmic magnitude compression, frequency weighting, and scaling.
We show that many commonly used input preprocessing techniques are redundant except magnitude compression.

Built on

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Then

K. Choi, G. Fazekas, and M. Sandler, “Automatic tagging using deep convolutional neural networks,” in The 17th International Society of Music Information Retrieval Conference, New York, USA . ISMIR, 2016
2016
Later among the works it cites.
J. Schlüter, “Learning to pinpoint singing voice from weakly labeled examples,” in Proceedings of the International Society for Music Information Retrieval Conference (ISMIR) , 2016, pp. 44–50
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S. F. Dodge and L. J. Karam, “Understanding how image quality affects deep neural networks,” CoRR , vol. abs/1604.04004, 2016. [Online]. Available: http://arxiv.org/abs/1604.04004
Original
2016
Later among the works it cites.
K. Choi, G. Fazekas, K. Cho, and M. Sandler, “A tutorial on deep learning for music information retrieval,” arXiv preprint arXiv:1709.04396 , 2017
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2017
Closest in time.
K. Choi, G. Fazekas, M. Sandler, and K. Cho, “Convolutional recurrent neural networks for music classification,” in 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing , 2017
2017
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B. McFee, M. McVicar, O. Nieto, S. Balke, C. Thome, D. Liang, E. Battenberg, J. Moore, R. Bittner, R. Yamamoto, D. Ellis, F.-R. Stoter, D. Repetto, S. Waloschek, C. Carr, S. Kranzler, K. Choi, P. Viktorin, J. F. Santos, A. Holovaty, W. Pimenta, and H. Lee, “librosa 0.5.0,” Feb. 2017. [Online]. Available: https://doi.org/10.5281/zenodo.293021
2017
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K. Choi, D. Joo, and J. Kim, “Kapre: On-gpu audio preprocessing layers for a quick implementation of deep neural network models with keras,” in Machine Learning for Music Discovery Workshop at 34th International Conference on Machine Learning , 2017
2017
Closest in time.

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