Multi-Level and Multi-Scale Feature Aggregation Using Sample-level Deep Convolutional Neural Networks for Music Classification
Sound
2017-06-22 v1 Machine Learning
Multimedia
Abstract
Music tag words that describe music audio by text have different levels of abstraction. Taking this issue into account, we propose a music classification approach that aggregates multi-level and multi-scale features using pre-trained feature extractors. In particular, the feature extractors are trained in sample-level deep convolutional neural networks using raw waveforms. We show that this approach achieves state-of-the-art results on several music classification datasets.
Cite
@article{arxiv.1706.06810,
title = {Multi-Level and Multi-Scale Feature Aggregation Using Sample-level Deep Convolutional Neural Networks for Music Classification},
author = {Jongpil Lee and Juhan Nam},
journal= {arXiv preprint arXiv:1706.06810},
year = {2017}
}
Comments
ICML Music Discovery Workshop 2017