  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.
