  Music prediction tasks range from predicting tags given a song or clip of
audio, predicting the name of the artist, or predicting related songs given a
song, clip, artist name or tag. That is, we are interested in every semantic
relationship between the different musical concepts in our database. In
realistically sized databases, the number of songs is measured in the hundreds
of thousands or more, and the number of artists in the tens of thousands or
more, providing a considerable challenge to standard machine learning
techniques. In this work, we propose a method that scales to such datasets
which attempts to capture the semantic similarities between the database items
by modeling audio, artist names, and tags in a single low-dimensional semantic
space. This choice of space is learnt by optimizing the set of prediction tasks
of interest jointly using multi-task learning. Our method both outperforms
baseline methods and, in comparison to them, is faster and consumes less
memory. We then demonstrate how our method learns an interpretable model, where
the semantic space captures well the similarities of interest.
