Johannes Mohr
Papers
1
Total Citations
14
H-Index
1
About
Johannes Mohr is a researcher whose work lies at the intersection of auditory perception, acoustic scene analysis, and machine learning. His primary focus is on understanding how machines can robustly detect and classify environmental sounds within complex, real-world auditory scenes—a challenge central to applications in hearing aids, autonomous systems, and smart environments. In his most cited work, “Robust Detection of Environmental Sounds in Binaural Auditory Scenes” (2017, 14 citations), Mohr systematically investigates how superimposed distractor sounds degrade classification performance, using simulations to isolate the impact of multiple simultaneous sources. This foundational study highlights his broader contribution: bridging the gap between idealized laboratory conditions and the messy, multi-source reality of everyday listening. By quantifying the vulnerabilities of current sound-type classifiers, Mohr’s research provides critical insights for designing more resilient auditory systems. His work is a vital step toward machines that hear as humans do—filtering signal from noise in a world that is never truly quiet.
Research Focus
Key Achievements
Top Papers
- 1Robust Detection of Environmental Sounds in Binaural Auditory Scenes14 citations · 2017