Accessible Fine-grained Data Representation via Spatial Audio
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866918438274859008 |
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| author | Liu, Can Jiang, Wenjie Ruan, Shaolun Hara, Kotaro Wang, Yong |
| author_facet | Liu, Can Jiang, Wenjie Ruan, Shaolun Hara, Kotaro Wang, Yong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_08979 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Accessible Fine-grained Data Representation via Spatial Audio Liu, Can Jiang, Wenjie Ruan, Shaolun Hara, Kotaro Wang, Yong Human-Computer Interaction Sound 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. |
| title | Accessible Fine-grained Data Representation via Spatial Audio |
| topic | Human-Computer Interaction Sound |
| url | https://arxiv.org/abs/2604.08979 |