  Recently, the end-to-end approach that learns hierarchical representations
from raw data using deep convolutional neural networks has been successfully
explored in the image, text and speech domains. This approach was applied to
musical signals as well but has been not fully explored yet. To this end, we
propose sample-level deep convolutional neural networks which learn
representations from very small grains of waveforms (e.g. 2 or 3 samples)
beyond typical frame-level input representations. Our experiments show how deep
architectures with sample-level filters improve the accuracy in music
auto-tagging and they provide results comparable to previous state-of-the-art
performances for the Magnatagatune dataset and Million Song Dataset. In
addition, we visualize filters learned in a sample-level DCNN in each layer to
identify hierarchically learned features and show that they are sensitive to
log-scaled frequency along layer, such as mel-frequency spectrogram that is
widely used in music classification systems.
