Synesthetic Algorithmic Composition: A Bio-Inspired Framework for Cross-Modal Creative Generation

Posted: Nov 12, 2024

• Dr. Elara Vance
• Prof. Kenji Tanaka
• Dr. Isabella Rossi

Abstract

This paper introduces a novel computational framework for synesthetic algorithmic composition that bridges auditory and visual creative domains through bio-inspired neural mechanisms. Unlike traditional generative systems that operate within single modalities, our approach models the neurological phenomenon of synesthesia to create intrinsically linked multi-modal artistic outputs. The core innovation lies in our development of Cross-Modal Resonance Networks (CMRNs), which adapt principles from sensory integration in biological systems to establish deep semantic mappings between musical structures and visual patterns. Our methodology incorporates three key components: a hierarchical temporal encoding system that captures musical features across multiple timescales, a spatial-semantic mapping engine that translates auditory patterns to visual representations, and a bidirectional generative interface that allows creative exploration in both directions. We trained our system on a curated dataset of 15,000 paired musical compositions and visual artworks spanning diverse genres and historical periods. Experimental results demonstrate that our framework generates coherent multi-modal outputs where musical compositions and visual artworks exhibit structural and emotional congruence that exceeds current state-of-the-art by 37% in human evaluation metrics. The system achieved particularly strong performance in maintaining thematic consistency across modalities, with human evaluators rating cross-modal coherence at 4.2/5.0 compared to 2.8/5.0 for baseline approaches. Furthermore, our analysis reveals emergent patterns in the learned mappings that align with established psychological theories of cross-modal perception, suggesting our computational model captures meaningful aspects of human creative cognition. This research contributes not only a novel technical framework for multi-modal creative generation but also provides computational insights into the nature of cross-modal artistic expression, with potential applications in interactive art installations, therapeutic environments, and enhanced creative tools for artists working across sensory boundaries.