Tim Ziemer

Simon Linke, Tim Ziemer

Kohonen Maps, aka. Self-organizing maps (SOMs) are neural networks that visualize a high-dimensional feature space on a low-dimensional map. While SOMs are an excellent tool for data examination and exploration, they inherently cause a loss of detail. Visualizations of the underlying data do not integrate well and, therefore, fail to provide an overall picture. Consequently, we suggest SOMson, an interactive sonification of the underlying data, as a data augmentation technique. The sonification increases the amount of information provided simultaneously by the SOM. Instead of a user study, we present an interactive online example, so readers can explore SOMson themselves. Its strengths, weaknesses, and prospects are discussed.

Tim Ziemer, Navid Mirzayousef Jadid

After decades of research, sonification is still rarely adopted in consumer electronics, software and user interfaces. Outside the science and arts scenes the term sonification seems not well known to the public. As a means of science communication, and in order to make software developers, producers of consumer electronics and end users aware of sonification, we developed, distributed, and promoted Tiltification. This smartphone app utilizes sonification to inform users about the tilt angle of their phone, so that they can use it as a torpedo level. In this paper we report on our app development, distribution and promotion strategies and reflect on their success in making the app in particular, and sonification in general, better known to the public. Finally, we give recommendations on how to develop, distribute and market sonification apps. This article is dedicated to research institutions without commercial interests.

Tim Ziemer, Nuttawut Nuchprayoon, Holger Schultheis

When operating a machine, the operator needs to know some spatial relations, like the relative location of the target or the nearest obstacle. Often, sensors are used to derive this spatial information, and visual displays are deployed as interfaces to communicate this information to the operator. In this paper, we present psychoacoustic sonification as an alternative interface for human-machine interaction. Instead of visualizations, an interactive sound guides the operator to the desired target location, or helps her avoid obstacles in space. By considering psychoacoustics --- i.e., the relationship between the physical and the perceptual attributes of sound --- in the audio signal processing, we can communicate precisely and unambiguously interpretable direction and distance cues along three orthogonal axes to a user. We present exemplary use cases from various application areas where users can benefit from psychoacoustic sonification.

Tim Ziemer, Holger Schultheis

Objective: Three perceptually orthogonal auditory dimensions for multidimensional and multivariate data sonification are identified and experimentally validated. Background: Psychoacoustic investigations have shown that orthogonal acoustical parameters may interfere perceptually. The literature hardly offers any solutions to this problem, and previous auditory display approaches have failed to implement auditory dimensions that are perceived orthogonally by a user. In this study we demonstrate how a location in three-dimensional space can be sonified unambiguously by the implementation of perceptually orthogonal psychoacoustic attributes in monophonic playback. Method: Perceptually orthogonal auditory attributes are identified from literature research and experience in music and psychoacoustic research. We carried out an experiment with 21 participants who identified sonified locations in two-dimensional space. Results: With just 5 minutes of explanation and exploration, naive users can interpret our multidimensional sonification with high accuracy. Conclusion: We identified a set of perceptually orthogonal auditory dimensions suitable for three-dimensional data sonification. Application: Three-dimensional data sonification promises blind navigation, e.g. for unmanned vehicles, and reliable real-time monitoring of multivariate data, e.g., in the patient care sector.