Accessible Fine-grained Data Representation via Spatial Audio

2026-04-10

AI Summary

This study addresses the limitations of existing pitch-based sonification methods, which struggle to effectively convey data signs and precise values, thereby restricting blind and low-vision users’ access to fine-grained information. To overcome this, the work introduces spatial audio directional encoding into data sonification for the first time, mapping data values to azimuthal sound locations on the horizontal plane. By leveraging the human auditory system’s spatial resolution capabilities, this approach enables a more nuanced auditory representation of data. User studies demonstrate that the proposed method significantly outperforms traditional pitch mapping in identifying data signs and exact values, performs comparably in trend recognition tasks, and is only slightly less effective in numerical comparison tasks. These results validate the method’s effectiveness and innovation in enhancing data accessibility for visually impaired users.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data visualization

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.

Problem

• accessible data representation
• fine-grained data perception
• spatial audio
• sonification
• blind and low-vision accessibility

Innovation

• spatial audio
• sonification
• fine-grained data representation
• accessibility
• blind and low-vision