  In the age of music streaming platforms, the task of automatically tagging
music audio has garnered significant attention, driving researchers to devise
methods aimed at enhancing performance metrics on standard datasets. Most
recent approaches rely on deep neural networks, which, despite their impressive
performance, possess opacity, making it challenging to elucidate their output
for a given input. While the issue of interpretability has been emphasized in
other fields like medicine, it has not received attention in music-related
tasks. In this study, we explored the relevance of interpretability in the
context of automatic music tagging. We constructed a workflow that incorporates
three different information extraction techniques: a) leveraging symbolic
knowledge, b) utilizing auxiliary deep neural networks, and c) employing signal
processing to extract perceptual features from audio files. These features were
subsequently used to train an interpretable machine-learning model for tag
prediction. We conducted experiments on two datasets, namely the MTG-Jamendo
dataset and the GTZAN dataset. Our method surpassed the performance of baseline
models in both tasks and, in certain instances, demonstrated competitiveness
with the current state-of-the-art. We conclude that there are use cases where
the deterioration in performance is outweighed by the value of
interpretability.
