Samarappuli, KumuduArdekani, ImanMohaghegh, MahsaSarrafzadeh, Abdolhossein2026-01-072026-01-072026-01-06Sensors, ISSN: 1424-8220 (Online), MDPI AG, 26(2), 371-371. doi: 10.3390/s260203711424-8220http://hdl.handle.net/10292/20457<jats:p>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.</jats:p>© 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.0301 Analytical Chemistry0502 Environmental Science and Management0602 Ecology0805 Distributed Computing0906 Electrical and Electronic EngineeringAnalytical Chemistry3103 Ecology4008 Electrical engineering4009 Electronics, sensors and digital hardware4104 Environmental management4606 Distributed computing and systems softwarebee bioacousticsynthetic datagenerative adversarial networksMarkov Chainsmart beekeepingJournal ArticleOpenAccess10.3390/s26020371