Accessible Fine-grained Data Representation via Spatial Audio
Abstract
Pitch-based sonification of quantitative data increases the accessibility of data visualizations that are otherwise inaccessible for blind and low-vision (BLV) individuals. We argue that, although pitch representations can reveal the coarse-grained information of data, such as data trend and value comparison, they cannot effectively convey the fine-grained details like the sign and exact value of individual data points. Informed by existing sound perception research, we propose a spatial audio-based approach by representing data values as the sound direction in the azimuth plane to achieve accessible fine-grained data representation. We conducted a user study with 26 participants (including 10 BLV participants) on four data perception tasks. The results show our approach significantly outperforms pitch representation on fine-grained data perception tasks like recognizing data signs and exact values, and performs similarly on data trend identification, despite its inferior accuracy on data value comparison.
Cite
@article{arxiv.2604.08979,
title = {Accessible Fine-grained Data Representation via Spatial Audio},
author = {Can Liu and Wenjie Jiang and Shaolun Ruan and Kotaro Hara and Yong Wang},
journal= {arXiv preprint arXiv:2604.08979},
year = {2026}
}
Comments
Accepted by IEEE Computer Graphics and Applications (IEEE CG&A)