This study aims to provide an insight into effective sonification design. There are currently no standardized design methods, allowing a creative development approach. Sonifcation has been implemented in many different applications from scientific data representation to novel styles of musical expression. This means that methods of practice can vary a greatly. The indistinct line between art and science might be the reason why sonification is still sometimes deemed by scientists with a degree of scepticism. Some wellestablished practitioners argue that it is poor design that renders sonifications meaningless, in-turn having an adverse effect on acceptance. To gain a deeper understanding about sonification research and development 11 practitioners were interviewed. They were asked about methods of sonification design and their insights. The findings present information about sonification research and development, and a variety of views regarding sonification design practice.

Sound is potentially an effective way of analysing data and it is possible to simultaneously interpret layers of sounds and identify changes. Multiple attempts to use sound with scientific data have been made, with varying levels of success. On many occasions this was done without including the end user during the development. In this study a sonified model of the 8 planets of our solar system was built and tested using an end user approach. The sonification was created for the Esplora Planetarium, which is currently being constructed in Malta. The data requirements were gathered from a member of the planetarium staff, and 12 end users, as well as the planetarium representative tested the sonification. The results suggest that listeners were able to discern various planetary characteristics without requiring any additional information. Three out of eight sound design parameters did not represent characteristics successfully. These issues have been identified and further development will be conducted in order to improve the model.

This study investigates the design and evaluation of a sonification designed to detect any planets orbiting within an asteroid belt of an exosolar system. The interface was designed for an astronomer who studies this phenomenon. User centered design methods were applied to create an accurate sonification of the data that could allow the astronomer to perceive possible planetary movements within an asteroid belt. The sonification was developed over three stages: A requirements gathering exercise inquiring about the data that the astronomer uses in her work. A design and development stage based on the findings of the requirements gathering and the third stage, an evaluation of the sonification design. The sonification effectively allowed the astronomer to immediately detect a planet orbiting within an asteroid belt. Multiple parameter mappings provide richer auditory stimuli that are more semantical to the user. The use of more familiar, natural sounding sound design led to a clearer comprehension of the dataset. The use of spatial mapping and movement allowed for immediate identification and understanding of the planet's course through the asteroid belt. CCS CONCEPTSHuman-centered computing →Interaction design→Interaction design process and methods →User centered design.

This study investigates the effectiveness of user design methods to create a sonification for an astronomer who analyses exoplanet meteorological data situated in habitable zones. Requirements about the astronomer's work, the dataset and how to sonify it utilising Grounded Theory were identified. Parameter mapping sonification was used to represent effective transiting radii measurements through subtractive synthesis and spatialization. The design was considered to be effective, allowing the instantaneous identification of a water feature overlooked on a visual graph, even when noise within the dataset overlapped the source signal. The results suggest that multiple parameter mappings provide richer auditory stimuli and semantic qualities in order to allow an improved understanding of the dataset.