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A Comparison of Audio Signal Preprocessing Methods for Deep Neural Networks on Music Tagging
lib:70c810b4ed287dc4 (v1.0.0)
Authors: Keunwoo Choi,György Fazekas,Kyunghyun Cho,Mark Sandler
ArXiv: 1709.01922
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1709.01922v2
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.
ArXiv: 1709.01922
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1709.01922v2
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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