  Automatic tagging of music is an important research topic in Music
Information Retrieval and audio analysis algorithms proposed for this task have
achieved improvements with advances in deep learning. In particular, many
state-of-the-art systems use Convolutional Neural Networks and operate on
mel-spectrogram representations of the audio. In this paper, we compare
commonly used mel-spectrogram representations and evaluate model performances
that can be achieved by reducing the input size in terms of both lesser amount
of frequency bands and larger frame rates. We use the MagnaTagaTune dataset for
comprehensive performance comparisons and then compare selected configurations
on the larger Million Song Dataset. The results of this study can serve
researchers and practitioners in their trade-off decision between accuracy of
the models, data storage size and training and inference times.
