Artificial Intelligence through machine learning to explore individual signatures in animal sounds

Our team investigates vocalizations of Savannah Sparrows (Passerculus sandwichensis) to understand how vocalizations are used during the breeding season. Our team previously detected a distinctive call type – the “twitter call”- that was poorly described and understood. The function of this call is unknown. The goal of this project is to explore if the twitter call contains an individual signature allowing birds to identify each other by call alone. Using recordings previously collected in the field, we will work with Dr. Nicolas Mathevon of the University of Saint-Etienne and ENES Bioacoustics Research Lab to use Artificial Intelligence to apply a new method developed by his team to detect individuality in the calls of individual birds. These methods allow a more accurate analysis of calls and increase our ability to detect the specific acoustic features that provide individuality. Knowing if this call is individually specific will increase our understanding of possible functions of this call and direct future experiments to test these functions. This research will additionally benefit Dr. Mathevon and his team by providing a full dataset to continue testing their new methodology, which has been previously used to explore more complex bird songs, rather than simple calls.

Faculty Supervisor:

Daniel Mennill

Student:

Partner:

Université Jean Monnet Saint-Étienne

Discipline:

Life Sciences

Sector:

Artificial Intelligence; Life Sciences (not health)

University:

University of Windsor

Program: