Accessible Fine-grained Data Representation via Spatial Audio
2026-04-10 • Human-Computer Interaction
Human-Computer InteractionSound
AI summaryⓘ
The authors studied ways to make data easier to understand for blind and low-vision people using sound. They found that using pitch (high or low sounds) helps show general trends in data but not detailed information like exact numbers or if a value is positive or negative. They tried a new method using spatial audio, where data values are shown by the direction a sound comes from. In tests, this new method helped people understand detailed data better than pitch alone, while still being good for seeing overall trends.
sonificationpitch representationspatial audioazimuth planedata visualization accessibilityblind and low-vision (BLV)data perceptioncoarse-grained informationfine-grained detailsuser study
Authors
Can Liu, Wenjie Jiang, Shaolun Ruan, Kotaro Hara, Yong Wang
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.