ISSBG2025 S LICCIARDI скачать в хорошем качестве

ISSBG2025 S LICCIARDI

SILVIA LICCIARDI & EMMANUEL CARRONA Playing Graphs. Music through Algorithms This study applies scientific and computational methods to the musical context, proposing a quantitative and replicable framework for the analysis of musical scores. Starting from digital encodings in MusicXML format, automatic data parsing enables the extraction and structured representation of melodic, textual, and harmonic information through data structures and directed graphs. Each musical element is modeled as a node enriched with metadata, allowing for precise and measurable representations of compositional features. By combining techniques from data mining, Graph Theory, and Graph Neural Networks, this approach aims to transform musical manuscripts into analyzable datasets, enabling automatic information retrieval and pattern recognition. Finally, this methodology lays the groundwork for developing generative models capable of exploring and simulating creative processes in composition Department of Engineering, University of Palermo, Italy