  Prevalent efforts have been put in automatically inferring genres of musical
items. Yet, the propose solutions often rely on simplifications and fail to
address the diversity and subjectivity of music genres. Accounting for these
has, though, many benefits for aligning knowledge sources, integrating data and
enriching musical items with tags. Here, we choose a new angle for the genre
study by seeking to predict what would be the genres of musical items in a
target tag system, knowing the genres assigned to them within source tag
systems. We call this a translation task and identify three cases: 1) no common
annotated corpus between source and target tag systems exists, 2) such a large
corpus exists, 3) only few common annotations exist. We propose the related
solutions: a knowledge-based translation modeled as taxonomy mapping, a
statistical translation modeled with maximum likelihood logistic regression; a
hybrid translation modeled with maximum a posteriori logistic regression with
priors given by the knowledge-based translation. During evaluation, the
solutions fit well the identified cases and the hybrid translation is
systematically the most effective w.r.t. multilabel classification metrics.
This is a first attempt to unify genre tag systems by leveraging both
representation and interpretation diversity.
