In this subproject we deal with challenges that are related to biological data. Our medical application focuses on automatic identification of voice disorders. Thereby, audio recordings of the acoustical voice signal are analysed with specialized software quantifying the amount of perturbation (noise) in the signal. Through automated feature extraction from the recordings and subsequent machine learning analysis, laryngeal movement patterns can be quantitatively captured and automatically classified according to different diagnostic classes (e.g. organic and functional dysphonia).
In our Multi-Agent Simulation of Evolution we investigate the biological phenomenon of aposematism (also referred to as warning coloration). This term describes the evolutionary strategy of certain animal species to indicate their unpalatability/toxicity to potential predators by developing skin colors and patterns that can be easily perceived by them. For tackling this interesting research challenge, we developed a distributed multi-agent model that simulates the dynamic interactions of predator and prey populations over time.