Machine learning detection of cetacean tonal calls without human annotations
Abstract
Deep learning has shown great potential in species-level detection and classification of marine mammal vocalizations. Recent studies suggest that training a neural network that performs well on unseen data often requires a large amount of analyst-annotated data, which are not only very expensive but also time-consuming to obtain for marine mammal species. We propose to develop new methods to learn such deep models from raw acoustic data with minimal human supervision in the loop. The proposed research will lead to a portable end-to-end whistle and moan detector capable of dealing with a wide variety of species, marine settings, weather conditions, etc. An option year provides for developing a classifier to extend the system to concurrently predict species from the detected tonals.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jun 09, 2021
- Source ID
- N000142112567
Entities
People
- Marie A. Roch
Organizations
- Office of Naval Research
- Salk Institute for Biological Studies
- United States Navy