Project Title: Automatically detecting emotional expressions in dynamic group interactions from audio signals
The goal of the successful candidate is to design signal processing and machine learning algorithms to automatically detect emotional expressions (individual affect and group mood) from recorded audio data. Challenges include speaker localization and diarization (often with overlapping speech), identification of discrete behaviors (e. g. laughter), and expansion to higher, more abstract levels of socioemotional behaviors (e. g., verbal expressions of support or disagreement) in order to detect convergent affective phenomena and emergent group mood.
This project is part of the interdisciplinary research group "Mechanisms of Change in Dynamic Social Interactions", which integrates fundamental science, innovative methods, and applications in psychology and computer science.
Please find the full job announcement with all details here
