Abstract
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.
| Original language | English (US) |
|---|---|
| Title of host publication | 2018 26th European Signal Processing Conference, EUSIPCO 2018 |
| Publisher | European Signal Processing Conference, EUSIPCO |
| Pages | 1870-1874 |
| Number of pages | 5 |
| ISBN (Electronic) | 9789082797015 |
| DOIs | |
| State | Published - Nov 29 2018 |
| Event | 26th European Signal Processing Conference, EUSIPCO 2018 - Rome, Italy Duration: Sep 3 2018 → Sep 7 2018 |
Publication series
| Name | European Signal Processing Conference |
|---|---|
| Volume | 2018-September |
| ISSN (Print) | 2219-5491 |
Other
| Other | 26th European Signal Processing Conference, EUSIPCO 2018 |
|---|---|
| Country | Italy |
| City | Rome |
| Period | 9/3/18 → 9/7/18 |
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering
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Cite this
Choi, K., Fazekas, G., Sandler, M., & Cho, K. (2018). A comparison of audio signal preprocessing methods for deep neural networks on music tagging. In 2018 26th European Signal Processing Conference, EUSIPCO 2018 (pp. 1870-1874). [8553106] (European Signal Processing Conference; Vol. 2018-September). European Signal Processing Conference, EUSIPCO. https://doi.org/10.23919/EUSIPCO.2018.8553106