Emmanouil Chourdakis
Senior Machine Learning Engineer at Epidemic Sound
Stockholm, Sweden
Summary
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Rockstar🎓
Top SchoolEmmanouil Chourdakis is a Senior Machine Learning Engineer based in Stockholm with a decade of experience specializing in digital audio signal processing, music information retrieval, and generative audio. He holds a PhD from Queen Mary University of London and has transitioned research ideas into production across roles at Nomono and Epidemic Sound, with early research experience at the BBC on automatic object-based audio mixing. Emmanouil blends rigorous academic training with practical engineering, evidenced by his hands-on QA and test automation contributions to the widely used audiomentations library to improve real-world robustness of audio ML pipelines. Comfortable moving between research, prototyping, and production, he focuses on delivering reliable audio ML systems that work beyond lab conditions. Colleagues value his attention to detail in test coverage and his ability to translate complex audio effects into verifiable, production-grade components.
10 years of coding experience
5 years of employment as a software developer
3rd High School of HeraklionDoctor of Philosophy - PhD, Doctor of Philosophy - PhD at Queen Mary University of LondonDiploma Degree Computer Engineering, Diploma Degree Computer Engineering at Technical University of Crete
Greek, English, French
Github Skills (6)
unit-testing10
scipy10
pytest10
python10
numpy10
testing10
Programming languages (11)
Github contributions (5)
iver56/audiomentations
Jan 2022 - Dec 2022
A Python library for audio data augmentation. Useful for making audio ML models work well in the real world, not just in the lab.
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:38 reviews, 40 commits, 10 PRs in 10 months
Contributions summary:Emmanouil primarily focused on enhancing the quality of the `audiomentations` library through rigorous testing. They implemented new tests for various audio effects, including high-shelf, low-shelf, band-pass, and band-stop filters, and added tests to check multi-channel audio processing. The contributions involved fixing test method names, adding magnitude tests to filters, and incorporating more robust test coverage to ensure the correctness of the audio data augmentation library.
pythonalbumentationsaudio-datadspaudio-effects
mmxgn/PyOpenAL-HRTF
Jun 2018 - Sep 2021
OpenAL bindings and audio playback for Python
Contributions:15 commits, 14 pushes in 3 years 3 months
pythonaudioplaybackplayback-audioopenal
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