AI/ML in Applied Marine Bioacoustics: Exploring the transfer of existing models from other domains

This project aims to answer the research question “can existing AI/ML models from other domains be applied to help address marine bioacoustics challenges?”

One of the key challenges is that marine bioacoustics lags behind terrestrial bioacoustics in the level of research attention and technical advancement. Additionally, bioacoustics as a field has been slower in leveraging AI/ML techniques compared to other domains, such as speech recognition and medical imaging.

The Mitacs intern will:
1. Expand the preliminary literature review undertaken during Winter 2025 through a Memorial University Professional Skills Development Program 60-hour Global Student Exchange placement.
2. Curate and clean a database of known/identified sound recordings for selected marine species, as well as recordings of marine environments containing many sounds (ambient, shipping, marine mammals, fishes, etc.).
3. Use the cleaned data to test the effectiveness of existing AI/ML models transferred from other domains.
4. Engage experts for challenge identification/confirmation, species selection, data provision and results verification.

Faculty Supervisor:

Carlos Bazan;Heather Ward

Student:

Partner:

Feaver's Lane Enterprises Inc.

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

Memorial University of Newfoundland

Program: