Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw Waveforms
This work addresses music classification for applications like tagging, but it is incremental as it extends existing end-to-end approaches to raw audio with minor architectural changes.
The paper tackles music auto-tagging by proposing sample-level deep convolutional neural networks that learn from raw waveforms at very small grains, achieving results comparable to state-of-the-art on the Magnatagatune and Million Song Dataset.
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.