Document Type

Article

Publication Date

2026

DOI

10.3390/s26020371

Publication Title

Sensors

Volume

26

Issue

2

Pages

371

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.

Rights

© 2026 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

Article states: "The source code and generated data samples used in this paper are publicly available at: https://github.com/KumuduS/Bioacoustic_MCWaveGAN (accessed on 1 November 2025).

Original Publication Citation

Samarappuli, K., Ardekani, I., Mohaghegh, M., & Sarrafzadeh, A. (2026). Markov chain wave generative adversarial network for bee bioacoustic signal synthesis. Sensors, 26(2), Article 371. https://doi.org/10.3390/s26020371

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