Markov Chain Wave Generative Adversarial Network for Bee Bioacoustic Signal Synthesis
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Samarappuli, Kumudu
Ardekani, Iman
Mohaghegh, Mahsa
Sarrafzadeh, Abdolhossein
Supervisor
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Journal Article
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MDPI AG
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 bioacoustic 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 alleviate data scarcity in bioacoustics and support intelligent monitoring in smart beekeeping, with broader applicability to other ecological and agricultural domains.Description
Keywords
0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering, Analytical Chemistry, 3103 Ecology, 4008 Electrical engineering, 4009 Electronics, sensors and digital hardware, 4104 Environmental management, 4606 Distributed computing and systems software, bee bioacoustic, synthetic data, generative adversarial networks, Markov Chain, smart beekeeping
Source
Sensors, ISSN: 1424-8220 (Online), MDPI AG, 26(2), 371-371. doi: 10.3390/s26020371
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© 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.