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

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Please contact us in case of a broken link from here

Authors Mark Sandler, Keunwoo Choi, Gy├Ârgy Fazekas, Kyunghyun Cho
Journal/Conference Name European Signal Processing Conference
Paper Category
Paper 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.
Date of publication 2017
Code Programming Language Jupyter Notebook

Copyright Researcher 2022