Hybrid Orchestration and Visual Perception: From Photographic Data to Orchestral Sound through Synthesis and Artificial Intelligence

This paper explores a bidirectional compositional model that systematically relates the visual–photographic domain to the sonic–orchestral domain through the combined use of artificial intelligence algorithms, sound synthesis, and hybrid orchestration. The research proposes an intermedial ecosystem in which photographic metadata belonging to the EXIF protocol, extracted via semantic image analysis, are translated into orchestration parameters for a variety of musical ensembles, spanning diverse styles and genres, from classical to contemporary music, from pop to jazz. As an initial phase of experimentation, a structured dataset of orchestral parameters (pitch, performance technique, dynamics, harmonic relationships) was constructed through the systematic analysis of a corpus of string orchestra scores from different styles and periods. The correspondence between EXIF parameters and orchestral parameters is established by querying large language models (LLMs) via API, employing chain-of-thought reasoning followed by musical validation, in a process that moves from the concrete instance towards abstract generalization. The forward flow, from photograph to sound, is articulated through hybrid orchestration for virtual symphonic ensemble, analogue synthesizers, and digital synthesizers. The reverse flow, from sound to image, the subject of a second research phase, involves the reconstruction of simulated EXIF metadata that feed generative visual models in order to produce corresponding synthetic images.