Affective visualization can influence how users perceive, interpret, and engage with data by embedding and conveying emotion through visual design. While sound is widely used in media to evoke emotions, little is known about how sonification can support affective visualization. In this work, we investigate how sonification can communicate emotion in uncertainty visualizations through a co-design study. Participants created two sonifications to accompany a visualization: one conveying the affective component of uncertainty and one conveying neutrality. Our findings show that uncertainty was commonly associated with wavy auditory qualities related to an ominous sentiment. On the other hand, neutrality was associated with clear and relaxing auditory qualities. These results provide insights for the design of visualizations that integrate sonification to communicate the affective component of uncertainty.
Figures & tables
Figure 1: The study tool consists of seven main parts: 1.) The visualization showing the yearly average German temperatures since 1881, including predicted temperatures up to 2100. 2.) Four white buttons for selecting a musical instrument (labeled as Sound 1-4 to reduce bias). 3.) One looping button for looping the sonification. 4.) Sliders to change the envelope length and wait time of the looping sonification. 5.) Sliders that allow changing the auditory parameters for the currently selected musical instrument. 6.) A green start button and drop-down menus allowing participants to map the pitch to the given data on the left side. The middle drop-down menu lets participants choose their pitch range based on pre-set options. The right drop-down menu allows for the change of the instrument, while the green button plays a sonification based on the options selected in the blue, orange, and red boxes. 7.) A pink button to play a pre-definded sonification, also called the baseline. The visualization is publicly available from Deutscher Wetterdienst ( \url https://www.dwd.de/DE/klimaumwelt/klimaatlas/klimaatlas_node.html).
Figure 2: The study procedure started with a tutorial of the tool, and continued with the creation of an uncertain and neutral sonification. This was followed up with filling in two questionnaires.
Figure 3: The distribution of musical inclination ratings of the 21 participants, which was recorded in a demographics questionnaire.
Figure 4: Number of participants mentioning the code.
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.
We present a general approach to visualizing uncertainty in static 2-D statistical graphics. If we treat a visualization as a function of its underlying quantities, uncertainty in those quantities induces a distribution over images. We show how to aggregate these images into a single visualization that represents the uncertainty. The approach can be viewed as a generalization of sample-based approaches that use overlay. Notably, standard representations, such as confidence intervals and bands, emerge with their usual coverage guarantees without being explicitly quantified or visualized. As a proof of concept, we implement our approach in the IID setting using resampling, provided as an open-source Python library. Because the approach operates directly on images, the user needs only to supply the data and the code for visualizing the quantities of interest without uncertainty. Through several examples, we show how both familiar and novel forms of uncertainty visualization can be created. The implementation is not only a practical validation of the underlying theory but also an immediately usable tool that can complement existing uncertainty-visualization libraries.
Bernarda Petek, David Nabergoj, Erik Štrumbelj
University of Ljubljana Faculty of Computer and Information Science Veˇcna pot 113, 1000 Ljubljana, Slovenia
Sonification operates across scientific, artistic, and design contexts, yet its identity remains conceptually ambiguous: is it music, a scientific method, or a hybrid practice? This paper presents a philosophical framework that treats sonification as a coherent, transcategorical practice. Drawing on Gustavo Bueno's materialist gnoseology, we identify its operative core through systematic data-to-sound relationships that define its technical identity. Complementing this with Gilbert Simondon's philosophy of individuation, we show conceptually how sonification adapts across contexts while preserving its operative structure. By distinguishing between technical identity and pragmatic use, this framework clarifies the relationship between sonification, music, and aesthetics, providing researchers and practitioners with a philosophical framework to navigate the field's diversity while maintaining its epistemic and operational integrity.
Rubén García-Benito
Instituto de Astrofísica de Andalucía (IAA), CSIC · PO Box 3004, 18080 Granada, Spain · Todos los Tonos y Ayres Ensemble