Three Orthogonal Dimensions for Psychoacoustic Sonification

TL;DR

This paper identifies and validates three perceptually orthogonal auditory dimensions for unambiguous three-dimensional data sonification, enabling applications like blind navigation and real-time multivariate data monitoring.

Contribution

It introduces a novel set of orthogonal psychoacoustic attributes for 3D data sonification, validated through experiments with naive users.

Findings

Naive users can interpret 3D sonification with high accuracy after brief training.

Identified auditory dimensions are perceptually orthogonal and suitable for multidimensional data.

Potential applications include blind navigation and real-time multivariate data monitoring.

Abstract

Objective: Three perceptually orthogonal auditory dimensions for multidimensional and multivariate data sonification are identified and experimentally validated. Background: Psychoacoustic investigations have shown that orthogonal acoustical parameters may interfere perceptually. The literature hardly offers any solutions to this problem, and previous auditory display approaches have failed to implement auditory dimensions that are perceived orthogonally by a user. In this study we demonstrate how a location in three-dimensional space can be sonified unambiguously by the implementation of perceptually orthogonal psychoacoustic attributes in monophonic playback. Method: Perceptually orthogonal auditory attributes are identified from literature research and experience in music and psychoacoustic research. We carried out an experiment with 21 participants who identified sonified locations in two-dimensional space. Results: With just 5 minutes of explanation and exploration, naive users can interpret our multidimensional sonification with high accuracy. Conclusion: We identified a set of perceptually orthogonal auditory dimensions suitable for three-dimensional data sonification. Application: Three-dimensional data sonification promises blind navigation, e.g. for unmanned vehicles, and reliable real-time monitoring of multivariate data, e.g., in the patient care sector.