Data Science Chair

    Our paper "Swarming Detection in Smart Beehives Using Auto Encoders for Audio Data" has been accepted at IWSSIP


    In our paper, we conduct an initial study to investigate the use of audio data from the We4Bee project in detecting bee swarming.

    Swarming is the natural mechanism by which bee colonies reproduce. Instead of preventing swarms as classical beekeeping does, precision beekeeping can provide alternatives through early notifications about impending swarms. In this work, we focus on identifying swarms and their early indicators in audio data captured from a smart beehive. However, the challenge with such domain-specific data is the low availability of labelled samples, the strong label imbalance, and the recording of undesired sources. We approach this challenge through a two- step setup: First, we use an auto encoder network to detect sounds from technical sources and then use it to clean data. Secondly, on the cleaned data we then employ a second network to identify event-related bee sounds. Using spectrogram features, our networks are able to reach a balanced accuracy score of more than 95% in the detection of special bee events. The findings of this initial study can serve as the starting point for further research on handling imbalanced data collections from smart, remote sensors that also contain undesired signals.