Exploring Graph Representation of Chorales
This is an incremental study applying existing graph methods to music data, potentially benefiting music analysis and machine learning in creative domains.
This paper tackled the problem of representing Bach chorales as graphs to explore music applications, using node embedding and collective classification methods, and found that the graph-based approach captures salient features for music.
This work explores areas overlapping music, graph theory, and machine learning. An embedding representation of a node, in a weighted undirected graph $\mathcal{G}$, is a representation that captures the meaning of nodes in an embedding space. In this work, 383 Bach chorales were compiled and represented as a graph. Two application cases were investigated in this paper (i) learning node embedding representation using \emph{Continuous Bag of Words (CBOW), skip-gram}, and \emph{node2vec} algorithms, and (ii) learning node labels from neighboring nodes based on a collective classification approach. The results of this exploratory study ascertains many salient features of the graph-based representation approach applicable to music applications.