Synesthetic Computing: A Multi-Modal Neural Architecture for Cross-Sensory Data Representation and Processing

Posted: Nov 10, 2024

• Dr. Elara Vance
• Prof. Kenji Tanaka
• Dr. Sofia Petrova

Abstract

This paper introduces Synesthetic Computing, a novel computational paradigm inspired by neurological synesthesia that enables cross-modal data representation and processing. Unlike traditional unimodal approaches, our framework allows data from one sensory domain (e.g., auditory) to be automatically represented and processed in another domain (e.g., visual or tactile). We developed a multi-layer neural architecture that learns cross-modal mappings through a combination of transformer-based attention mechanisms and generative adversarial networks. Experimental results demonstrate that our approach achieves 89.3% accuracy in cross-modal classification tasks and enables novel applications in accessibility technology, creative computing, and data visualization. The system successfully translates musical compositions into visual art while preserving emotional content, converts textual descriptions into tactile patterns, and transforms visual scenes into auditory experiences with 76.8% semantic preservation according to human evaluators.