Wind Turbine Audibility - Aercoustics collab


This project presents a unique opportunity to for developers contribute to environmental sustainability and simultaneously gain valuable experience in machine learning for audio applications.

Wind energy in Canada accounts for 3.5 per cent of electricity generation and is the second most important renewable energy source in the region. However, during the commissioning of a new wind turbine generation facility in Ontario, provincial regulations require that a study be performed to assess the noise pollution impact on surrounding residents. The study involves collecting thousands of sound level datapoints over the course of months, using these to establish sound level as a function of wind speed with turbine operation and classify datapoints on audibility. The volume of data required to be collected under various weather and operating conditions, in addition to stringent data quality requirements, means that the measurement campaign is long and subsequent analysis is very rigorous and costly.

To automate this process, UTMIST will be working with sound consulting company - Aercoustics - to provide an efficient ML solution. The timeline of this project is expected to span across the Winter and Summer term. We are recruiting for both a director and developers that could commit at least 6 month of time for this project. You will be expected to design the ML implementation plans based on Aercoustics’ business demands, deliver a minimum viable product (with command line interface) and meet their regular KPIs.

The Team

Tanmay Bishnoi
Hassan Khurram
Daniel Kwan
Greatman Okonkwo