Lecturer In Audio Signal Processing at Queen Mary University of London
London, England, United Kingdom
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Johan Pauwels is a Lecturer in Audio Signal Processing based in London with 15 years of experience bridging music information retrieval, musical signal processing and machine learning. He combines academic leadership and teaching with hands-on research and software development, having held roles from postdoc to visiting researcher at institutions including Queen Mary and Imperial College. Johan is a practical open-source contributor: he improved cross-platform training workflow in CXXNet/MXNet, enhanced audio I/O and edge-case handling in madmom, and modernized UX for the webMUSHRA listening test. His background in AI and electrical engineering underpins a knack for making audio research reproducible and production-ready across platforms. He often tackles gritty compatibility and usability problems that others overlook, such as non-streamable MP4 audio handling and Likert-scale visualization improvements. Johan holds a PhD in engineering from Ghent and a postgraduate certificate in academic practice, combining rigorous research with effective teaching.
15 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Engineering, Doctor of Philosophy (Ph.D.), Engineering at Ghent University
Master, Artificial Intelligence, Master, Artificial Intelligence at Katholieke Universiteit Leuven
Postgraduate Certificate, Academic Practice, Postgraduate Certificate, Academic Practice at Queen Mary University of London
a MUSHRA compliant web audio API based experiment software
Role in this project:
Front-end Developer
Contributions:3 reviews, 10 commits, 1 PR in 1 month
Contributions summary:Johan primarily focused on front-end development tasks, improving the user interface and user experience of the web application. They addressed whitespace and tab/space inconsistencies and also modified the display of Likert scale and trial results. The user also added features to the Likert Single Stimulus pages, including waveform visualization and the ability to limit stimuli.
Contributions:6 commits, 5 PRs, 8 comments in 2 years 3 months
Contributions summary:Johan primarily focused on improving the audio I/O capabilities of the `madmom` library. They implemented functionality to read audio from file objects created in memory, adding both the necessary code changes and corresponding tests to ensure correct behavior. Additionally, the user addressed Python 2 compatibility issues and handled a specific edge case related to M4A files within non-streamable MP4 containers.
signalpythonmidiscipysignal-processing
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Johan Pauwels - Lecturer In Audio Signal Processing at Queen Mary University of London