Publication
Demo: Enhancing Wildlife Acoustic Data Annotation Efficiency through Transfer and Active Learning
Hannes Kath; Patricia P. Serafin; Ivan B. Campos; Thiago Gouvea; Daniel Sonntag
In: Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. International Joint Conference on Artificial Intelligence (IJCAI-2024), located at IJCAI, August 03-09, Jeju. International Joint Conference on Artificial Intelligence (IJCAI-2024), August 3-9, Jeju, Korea, Republic of, International Joint Conferences on Artificial Intelligence, 8/2024.
Abstract
Passive Acoustic Monitoring (PAM) has become a key technology in wildlife monitoring, generating large amounts of acoustic data. However, the effective application of machine learning methods for sound event detection in PAM datasets is highly dependent on the accessibility of annotated data, a process that can be labour intensive. As a team of domain experts and machine learning researchers, in this paper we present a no-code annotation tool designed for PAM datasets that incorporates transfer learning and active learning strategies to address the data annotation challenge inherent in PAM. Transfer learning is applied to use pre-trained models to compute meaningful embeddings from the PAM audio files. Active learning iteratively identifies the most informative samples and then presents them to the user for annotation. This iterative approach improves the performance of the model compared to random sample selection. In a preliminary evaluation of the tool, a domain expert annotated part of a real PAM data set. Compared to conventional tools, the workflow of the proposed tool showed a speed improvement of 2-4 times. Further enhancements, such as the incorporation of sound examples, have the potential to further improve efficiency.