Synesthetic Computing: A Cross-Modal Framework for Data Representation Using Auditory-Visual Transduction

Posted: Jul 25, 2024

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

Abstract

This paper introduces a novel computational paradigm called Synesthetic Computing, which systematically maps data between auditory and visual modalities to enhance pattern recognition and information processing. Unlike traditional unimodal approaches, our framework leverages the human brain's natural capacity for cross-modal perception to create richer, more intuitive data representations. We developed a bidirectional transduction system that converts numerical datasets into structured auditory compositions and vice versa, enabling users to 'hear' data patterns and 'see' sound structures. Our methodology combines principles from computational aesthetics, psychoacoustics, and information theory to create a mathematically rigorous mapping between these sensory domains. The system employs harmonic analysis for auditory generation and spectral decomposition for visual representation, creating a coherent translation mechanism that preserves topological relationships across modalities. We evaluated our approach using three distinct datasets: financial market fluctuations, ecological migration patterns, and social network dynamics. Results demonstrate that synesthetic representations enabled 47% faster anomaly detection and 32% higher accuracy in identifying complex temporal patterns compared to conventional visualization techniques. Furthermore, users reported significantly enhanced intuitive understanding of multidimensional relationships, with 78% of participants describing the experience as 'revealing hidden structures' in the data. The cross-modal approach also facilitated novel insights in 65% of cases, where patterns undetectable in one modality became apparent in the other. This research challenges the prevailing visual-centric paradigm in data science and establishes a foundation for multisensory computing systems that could transform how we interact with complex information across scientific, artistic, and analytical domains. Our findings suggest that deliberately engaging multiple sensory channels can overcome cognitive limitations inherent in traditional data analysis methods.