Markov Chain Wave Generative Adversarial Network for bee bioacoustic signal synthesis
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Authors
Samarappuli , Kumudu
Ardekani, Iman
Mohaghegh, M.
Sarrafzadeh, Abdolhossein
Ardekani, Iman
Mohaghegh, M.
Sarrafzadeh, Abdolhossein
Author ORCID Profiles (clickable)
Degree
Grantor
Date
2026-01-06
Supervisors
Type
Journal Article
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
honeybees (Apis mellifera)
audio data processing systems
beehive monitoring
Generative Adversarial Networks (GANs)
AI in agriculture
audio data processing systems
beehive monitoring
Generative Adversarial Networks (GANs)
AI in agriculture
ANZSRC Field of Research Code (2020)
Citation
Samarappuli, K., Ardekani, I., Mohaghegh, M., & Sarrafzadeh, A. (2026). Markov Chain Wave Generative Adversarial Network for bee bioacoustic signal synthesis, Sensors, 26(2), 371:1-24. https://doi.org/10.3390/s26020371
Abstract
This paper presents a framework for synthesizing bee bioacoustic signals associated with hive events. While existing approaches like WaveGAN have shown promise in audio generation, they often fail to preserve the subtle temporal and spectral features of bioacous tic signals critical for event-specific classification. The proposed method, MCWaveGAN, extends WaveGAN with a Markov Chain refinement stage, producing synthetic signals that more closely match the distribution of real bioacoustic data. Experimental results show that this method captures signal characteristics more effectively than WaveGAN alone. Furthermore, when integrated into a classifier, synthesized signals improved hive status prediction accuracy. These results highlight the potential of the proposed method to allevi ate data scarcity in bioacoustics and support intelligent monitoring in smart beekeeping, with broader applicability to other ecological and agricultural domains.
(This article belongs to the Special Issue AI, Sensors and Algorithms for Bioacoustic Applications)
Publisher
MDPI (Multidisciplinary Digital Publishing Institute)
Permanent link
Link to ePress publication
DOI
https://doi.org/10.3390/s26020371
Copyright holder
©2026by the authors. Licensee MDPI, Basel, Switzerland
Copyright notice
CC BY Attribution 4.0 International