Multi-Level and Multi-Scale Feature Aggregation Using Sample-level Deep Convolutional Neural Networks for Music Classification
Published in
International Conference on Machine Learning(2017)External Links:
TL
TL;DR
This work proposes a music classification approach that aggregates multi-level and multi-scale features using pre-trained feature extractors trained in sample-level deep convolutional neural networks using raw waveforms.
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.
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Multi-Level and Multi-Scale Feature Aggregation Using Pretrained Convolutional Neural Networks for Music Auto-Tagging
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