AppMAIS Audio Data Labeling Application

• Main Authors: Campell, Christopher, Tashakkori, Rahman, Somer, Alex, Richardson, Logan, Simons-Rudolph, Aedan
• Format: Conference Proceeding
• Language: English
• Subjects: Biomedical monitoring; honey bee swarm; Informatics; Labeling; Machine learning; Monitoring; piping detection; Precision apiculture; Recording; swarm detection; User interfaces; visualization
• ISSN: 1558-058X
• DOI: 10.1109/SoutheastCon52093.2024.10500191

The Appalachian Multi-purpose Apiary Informatics System (AppMAIS) research project has monitored the behavior of some honey beehives in a small region of Western North Carolina in the past two years. With over two million audio files sampled across twenty-nine research hives, manually labeling each recording was infeasible. The presence or absence of piping, a bioacoustic signal emitted by honey bees as a precursor to a swarm event provides critical information for beekeepers. The AppMAIS Audio Data Labeling Application (ADLA) was developed in our lab to label the vast audio data. To date, over thirty-eight thousand recordings have been labeled using this application. We were able to train a Machine Learning model to detect piping with high accuracy. This paper describes the implementation of the labeling application and its user interface in detail. The application has helped us identify several swarms and backtrack the chain of events several days before they occurred, allowing us to utilize our trained model as a preventive measure.