2022/01/27 by Somnuk Phon-Amnuaisuk, Phon-Amnuaisuk, Somnuk
Computer Science · Neuroscience · #Artificial Intelligence (cs.AI) #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Music Technology and Sound Studies #Music and Audio Processing #Neuroscience and Music Perception #Sound (cs.SD) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2201.11745
openalex publication_date 2022/01/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This work explores areas overlapping music, graph theory, and machine learning. An embedding representation of a node, in a weighted undirected graph 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 Continuous Bag of Words (CBOW), skip-gram, and 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.