Graduation: Automation of Sound level predictions

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At Damen, much effort is put into designing silent and comfortable ships. Therefore, sound level predictions are made to judge the feasibility of each design. This of course asks for accurate prediction methods.

Develop a method and build a tool that analyzes an audio recording from a sea trial and automatically:

• Identifies acoustic sources (diesel engine, electric motor, propeller, cavitation, turbulence, pump/HVAC, etc.).
• Determines source characteristics (rotational speed/RPM, number of blades/cylinders, etc.).
• Quantifies the acoustic contribution per source (level, spectrum, and derived metrics).
• Performs source separation (acoustic representation of the contribution of each detected source).

As an intern, you will:

• Create a model that classifies acoustic sources in a time domain recording of pressures, accelerations, etc.
• Define source‑specific output such as RPM, number of blades, and number of cylinders.
• Create a prototype application (Python/MATLAB).
• Deliver documentation and evaluation based on real Damen sea trial data.

• Affinity with acoustics such as spectrograms and harmonic analysis.
• Familiarity with signal processing and Machine Learning frameworks.
• Analytical oriented mindset.
• Good programming/scripting skills in Matlab/Python.
• Mastery of the English language (spoken and written).

• Academic level mentoring throughout the internship.
• Internship compensation and travel allowance in line with company policy.
• A project with direct relevance to multiple business functions and future digital products.
• Potential for internal rollout, and possibly a publication or conference contribution depending on results.

International students without accommodation in the Netherlands are not eligible.