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arXiv · 2604.08979

Accessible Fine-grained Data Representation via Spatial Audio

Can Liu·Wenjie Jiang·Shaolun Ruan·Kotaro Hara·Yong Wang
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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.

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Can Liu, Wenjie Jiang, Shaolun Ruan, Kotaro Hara, Yong Wang. 2026-04-10. Accessible Fine-grained Data Representation via Spatial Audio. https://arxiv.org/abs/2604.08979

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