Using Pairwise Link Prediction and Graph Attention Networks for Music Structure Analysis
Abstract
The task of music structure analysis has been mostly
addressed as a sequential problem, by relying on the internal homogeneity of musical sections or their repetitions.
In this work, we instead regard it as a pairwise link prediction task. If for any pair of time instants in a track, one
can successfully predict whether they belong to the same
structural entity or not, then the underlying structure can
be easily recovered. Building upon this assumption, we
propose a method that first learns to classify pairwise links
between time frames as belonging to the same section (or
segment) or not. The resulting link features, along with
node-specific information, are combined through a graph
attention network. The latter is regularized with a graph
partitioning training objective and outputs boundary locations between musical segments and section labels. The
overall system is lightweight and performs competitively
with previous methods. The evaluation is done on two
standard datasets for music structure analysis and an ablation study is conducted in order to gain insight on the
role played by its different components.
Domains
Mathematics [math]Origin | Publisher files allowed on an open archive |
---|