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

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

6 Sep 2017arXiv:1709.01922archive 2025-07-28

Keunwoo Choi, György Fazekas, Kyunghyun Cho, Mark Sandler

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.

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GorillaBus/urban-audio-classifier mentioned on GitHubtfLGPL-3.0 report

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Music Tagging

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