Graduation: Automation of Sound level predictions
Core
Develop an automated methodology and prototype tool to analyze sea trial audio/acceleration data, identify acoustic sources (engines, propellers, cavitation), and quantify their characteristics for ship noise prediction.
Role type
Intern, Applied Acoustics & Signal Processing
Builds
A prototype application for automated acoustic source identification and characterization
Domain
Naval Architecture / Maritime Technology / Acoustics
Deliverable
production ML models | product features
Required skills
Signal processing, Machine Learning frameworks, Python/MATLAB programming, Spectrogram analysis, Harmonic analysis
Preferred skills
Naval Architecture, Maritime Technology, Mechanical Engineering, Applied Physics
Technologies
Python, MATLAB
Responsibilities
Create a model to classify acoustic sources in time domain recordings, Define source-specific outputs (RPM, blade/cylinder counts), Create a prototype application, Deliver documentation and evaluation based on real sea trial data
Seniority
Intern