  The lack of data tends to limit the outcomes of deep learning research,
particularly when dealing with end-to-end learning stacks processing raw data
such as waveforms. In this study, 1.2M tracks annotated with musical labels are
available to train our end-to-end models. This large amount of data allows us
to unrestrictedly explore two different design paradigms for music
auto-tagging: assumption-free models - using waveforms as input with very small
convolutional filters; and models that rely on domain knowledge - log-mel
spectrograms with a convolutional neural network designed to learn timbral and
temporal features. Our work focuses on studying how these two types of deep
architectures perform when datasets of variable size are available for
training: the MagnaTagATune (25k songs), the Million Song Dataset (240k songs),
and a private dataset of 1.2M songs. Our experiments suggest that music domain
assumptions are relevant when not enough training data are available, thus
showing how waveform-based models outperform spectrogram-based ones in
large-scale data scenarios.
