A deep learning based method for honeybee sound monitoring and analysis

Ksiazek P, Król-Nowak A, Stefanowska E. A deep learning based method for honeybee sound monitoring and analysis. In: AES Convention 154. Audio Engineering Society; 2023. Paper 58. Available from: https://aes.org/publications/elibrary-page/?id=22083

@inproceedings{Ksiazek2023_22083,
  author = {Ksiazek, Piotr and Król-Nowak, Aleksandra and Stefanowska, Emilia},
  title = {{A deep learning based method for honeybee sound monitoring and analysis}},
  booktitle = {AES Convention 154},
  note = {Paper 58},
  year = {2023},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=22083}
}

TY  - CPAPER
TI  - A deep learning based method for honeybee sound monitoring and analysis
AU  - Ksiazek, Piotr
AU  - Król-Nowak, Aleksandra
AU  - Stefanowska, Emilia
T2  - AES Convention 154
M1  - Paper 58
PY  - 2023
DA  - 2023/05/06
UR  - https://aes.org/publications/elibrary-page/?id=22083
PB  - Audio Engineering Society
LA  - en
AB  - This paper is a proof of concept application of deep learning technologies towards honeybee colony monitoring. A goal was set to determine internal beehive temperature only through analysis of sound signals produced by the hive. Such a goal was not attempted before. Signals were acquired using an experimental monitoring station, which gathered data from both inside and outside the beehive, as well as recorded temperature inside the beehive. Features extracted from those signals were mel frequency cepstral coefficients and power spectral density. A deep learning convolutional network was employed in the analysis. All tested methods achieved satisfactory results and allowed sufficiently correct prediction of temperatures inside the beehive based on signals recorded by both an internal and an external microphone. Differences of results obtained using external and internal measurements were similar. This proof of concept serves as an indication of future research possibilities concerning automated acoustic monitoring of honeybee families. Such possibilities lie mainly within honeybee health monitoring to which goal this paper’s findings may be of use.
ER  -