Bio-Acoustical Machine Learning Recognition of Animal Calls
Frederic Theunissen, Professor
Psychology
Closed. This professor is continuing with Fall 2024 apprentices on this project; no new apprentices needed for Spring 2025.
Our laboratory studies vocal communication in animals and would like to develop an automatic classifier for bird calls using advanced machine learning techniques.
Role: The URAP will be writing Python code to implement supervised classification algorithms that will decode the call type and caller id for vocalizations produced by zebra finches and canaries. In addition to further gaining expertise in machine learning techniques, the URAP will also signal processing methods used for sound analyses and become familiar with current topics in animal communication.
Qualifications: Undergraduate research apprentices need to have significant programming experience using Python, including the use of scikit-learn and Jupiter notebooks. Experience with sound analyses techniques is a plus. Fun position for CS majors who want to be involved in an applied project. Also great for biologists interested in animal communication and have some machine learning experience.
Day-to-day supervisor for this project: Logan Thomas, Graduate Student
Hours: 6-8 hrs
Off-Campus Research Site: This is a remote project that can be performed at home.
Biological & Health Sciences