  Music genres allow to categorize musical items that share common
characteristics. Although these categories are not mutually exclusive, most
related research is traditionally focused on classifying tracks into a single
class. Furthermore, these categories (e.g., Pop, Rock) tend to be too broad for
certain applications. In this work we aim to expand this task by categorizing
musical items into multiple and fine-grained labels, using three different data
modalities: audio, text, and images. To this end we present MuMu, a new dataset
of more than 31k albums classified into 250 genre classes. For every album we
have collected the cover image, text reviews, and audio tracks. Additionally,
we propose an approach for multi-label genre classification based on the
combination of feature embeddings learned with state-of-the-art deep learning
methodologies. Experiments show major differences between modalities, which not
only introduce new baselines for multi-label genre classification, but also
suggest that combining them yields improved results.
