—Swarming is the natural mechanism by which bee
colonies reproduce, but for beekeepers it is a challenge. Precision
beekeeping can aid their work 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. 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 mechanical 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 99 % 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 sensor environments that also contain undesired
signals.
%0 Conference Paper
%1 janetzky2023swarming
%A Janetzky, Pascal
%A Schaller, Melanie
%A Krause, Anna
%A Hotho, Andreas
%B 2023 30th International Conference on Systems, Signals and Image Processing (IWSSIP)
%D 2023
%K 2023 from:jascal_panetzky machine-learning myown we4bee we4bee_audio
%P 1-5
%R 10.1109/IWSSIP58668.2023.10180253
%T Swarming Detection in Smart Beehives Using Auto Encoders for Audio Data
%X —Swarming is the natural mechanism by which bee
colonies reproduce, but for beekeepers it is a challenge. Precision
beekeeping can aid their work 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. 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 mechanical 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 99 % 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 sensor environments that also contain undesired
signals.
@inproceedings{janetzky2023swarming,
abstract = {—Swarming is the natural mechanism by which bee
colonies reproduce, but for beekeepers it is a challenge. Precision
beekeeping can aid their work 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. 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 mechanical 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 99 % 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 sensor environments that also contain undesired
signals.},
added-at = {2023-09-21T10:41:47.000+0200},
author = {Janetzky, Pascal and Schaller, Melanie and Krause, Anna and Hotho, Andreas},
biburl = {https://www.bibsonomy.org/bibtex/258cc30c189bcf7d38fcbbf50c031a85e/hotho},
booktitle = {2023 30th International Conference on Systems, Signals and Image Processing (IWSSIP)},
doi = {10.1109/IWSSIP58668.2023.10180253},
interhash = {6ffc8218c5defb7a76d5cdfed678ce95},
intrahash = {58cc30c189bcf7d38fcbbf50c031a85e},
keywords = {2023 from:jascal_panetzky machine-learning myown we4bee we4bee_audio},
pages = {1-5},
timestamp = {2023-09-21T15:12:07.000+0200},
title = {Swarming Detection in Smart Beehives Using Auto Encoders for Audio Data},
year = 2023
}