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. Two architectures were developed: an unconditional GAN for generating long, high-fidelity audio, and a conditional GAN that incorporates time-of-day information to generate shorter samples reflecting diurnal honeybee activity patterns. The generated audio exhibits both spectral and temporal properties similar to real recordings, as confirmed by statistical analysis performed during the experiment. This research has implications for scientific research in honeybee colony health monitoring as well as apitherapy research. and artistic endeavours, for example in sound design and immersive soundscape creation, the trained generator model is publicly available on the project’s website.
