AppMAIS Audio Data Labeling Application

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...

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Bibliographic Details
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
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Summary: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.
ISSN:1558-058X
DOI:10.1109/SoutheastCon52093.2024.10500191