  Version identification (VI) has seen substantial progress over the past few
years. On the one hand, the introduction of the metric learning paradigm has
favored the emergence of scalable yet accurate VI systems. On the other hand,
using features focusing on specific aspects of musical pieces, such as melody,
harmony, or lyrics, yielded interpretable and promising performances. In this
work, we build upon these recent advances and propose a metric learning-based
system systematically leveraging four dimensions commonly admitted to convey
musical similarity between versions: melodic line, harmonic structure, rhythmic
patterns, and lyrics. We describe our deliberately simple model architecture,
and we show in particular that an approximated representation of the lyrics is
an efficient proxy to discriminate between versions and non-versions. We then
describe how these features complement each other and yield new
state-of-the-art performances on two publicly available datasets. We finally
suggest that a VI system using a combination of melodic, harmonic, rhythmic and
lyrics features could theoretically reach the optimal performances obtainable
on these datasets.
