Accessible Fine-grained Data Representation via Spatial Audio

TL;DR

This paper introduces a spatial audio method for data sonification that improves fine-grained data perception for blind and low-vision users, surpassing traditional pitch-based methods in recognizing data signs and exact values.

Contribution

The authors propose a novel spatial audio approach for data sonification that enhances fine-grained data perception, addressing limitations of pitch-based representations.

Findings

Spatial audio significantly improves recognition of data signs and exact values.

Approach performs similarly to pitch-based methods in data trend identification.

Method outperforms pitch representation in fine-grained data perception tasks.

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.