Synesthetic Computing: A Cross-Modal Framework for Visual-Auditory Data Representation and Analysis

Posted: Oct 25, 2025

• Dr. Elara Vance
• Prof. Kaito Tanaka
• Dr. Sofia Petrov

Abstract

This paper introduces synesthetic computing, a novel computational paradigm that systematically maps data between visual and auditory modalities to enhance human-computer interaction and data analysis capabilities. Unlike traditional unimodal approaches, our framework leverages the natural human capacity for cross-modal perception to create richer, more intuitive data representations. We developed a bidirectional mapping system that translates visual patterns into auditory sequences and vice versa, enabling users to perceive and analyze complex datasets through both visual and auditory channels simultaneously. Our methodology incorporates principles from cognitive science, information theory, and computational aesthetics to create meaningful cross-modal correspondences that preserve essential data characteristics while enabling novel analytical perspectives. The framework includes algorithms for color-to-sound frequency mapping, spatial position to temporal sequencing, and texture to timbre transformation, with careful attention to maintaining data integrity across modalities. We evaluated our approach through three distinct applications: multivariate financial data analysis, network security monitoring, and scientific visualization of climate patterns. Results from controlled user studies with 45 participants demonstrated significant improvements in pattern recognition accuracy (32% increase compared to visual-only analysis) and anomaly detection speed (41% faster response times) when using the synesthetic approach. Furthermore, users reported enhanced engagement and reduced cognitive fatigue during extended analytical sessions. The cross-modal representations also revealed patterns that were less apparent in single-modality displays, particularly in high-dimensional datasets where traditional visualization techniques struggle with information density. Our findings suggest that synesthetic computing represents a promising direction for human-centered data analysis, offering both practical benefits for complex data interpretation and theoretical insights into cross-modal information processing. This work establishes foundational principles for multisensory computing interfaces and opens new possibilities for accessible data analysis tools that leverage the full spectrum of human perceptual capabilities.