Accessible Fine-grained Data Representation via Spatial Audio
This work addresses accessibility challenges for blind and low-vision individuals in data visualization by enabling fine-grained data perception, though it is incremental as it builds on existing sound perception research.
The paper tackled the problem of conveying fine-grained data details like exact values and signs to blind and low-vision individuals, proposing a spatial audio approach that significantly outperformed pitch-based sonification in fine-grained tasks, with results showing improved accuracy in recognizing data signs and exact values in a user study with 26 participants.
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.