In this talk I will discuss a generalization of the concept of musical spaces as networks, and derive functional principles of compositional design by the direct analysis of the network topology. This approach provides a novel framework for the analysis and quantification of similarity of musical objects and structures, and suggests a way to relate such measures to the human perception of different musical entities. Finally, the analysis of a single work or a corpus of compositions as complex networks provides alternative ways of interpreting the compositional process of a composer by quantifying emergent behaviors with well-established statistical mechanics techniques. Interpreting the latter as probabilistic randomness in the network, we can model behaviors that mimic algorithmically the music composition process.
This work provides a novel perspective on the foundations of music theory, analysis and composition in the broader framework of network theory, machine learning and artificial intelligence.