Honeybee sound generation using Machine learning techniques

Książek P, Libal U. Honeybee sound generation using Machine learning techniques. In: Express Paper. Audio Engineering Society; 2025.pp. 320:1-. Available from: https://aes.org/publications/elibrary-page/?id=22871

@inproceedings{Ksiazek2025_22871,
  author = {Książek, Piotr and Libal, Urszula},
  title = {{Honeybee sound generation using Machine learning techniques}},
  pages = {320:1-},
  year = {2025},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=22871}
}

TY  - CPAPER
TI  - Honeybee sound generation using Machine learning techniques
AU  - Książek, Piotr
AU  - Libal, Urszula
SP  - 1
M1  - Paper 320
PY  - 2025
DA  - 2025/05/12
UR  - https://aes.org/publications/elibrary-page/?id=22871
PB  - Audio Engineering Society
LA  - en
AB  - The Honeybee is an insect known to almost all human beings around the world. The sounds produced by bees is a ubiquitous staple of the soundscape of the countryside and forest meadows, bringing an air of natural beauty to the perceived environment. Honeybee-produced sounds are also an important part of apitherapeutic experiences, where the close-quarters exposure to honeybees proves beneficial to the mental and physical well-being of humans. This research investigates the generation of synthetic honeybee buzzing sounds using Conditional Generative Adversarial Networks (cGANs). Trained on a comprehensive dataset of real recordings collected both inside and outside the beehive during a long-term audio monitoring session. The models produce diverse and realistic audio samples. A conditional GAN architecture was developed, which incorporates time-of-day information to generate two-second sound clips reflecting diurnal honeybee activity patterns. The generated audio exhibits both spectral and temporal properties similar to real recordings in given network and time of day variants, as confirmed by statistical analysis performed during the experiment. This research has some implications for scientific research in honeybee colony health monitoring as well as potential future use in apitherapy research and artistic endeavors, for example in sound design and immersive soundscape creation, the trained generator model is publicly available on the projects website.
ER  -