Markov Chain Wave Generative Adversarial Network for Bee Bioacoustic Signal Synthesis.

Kumudu Samarappuli, Iman Ardekani, Mahsa Mohaghegh, Abdolhossein Sarrafzadeh

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Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Kumudu SamarappuliSchool of Computing, Electrical Engineering and Applied Technology, Unitec Institute of Technology, Auckland 1025, New Zealand.

Iman ArdekaniSchool of Computing, Electrical Engineering and Applied Technology, Unitec Institute of Technology, Auckland 1025, New Zealand.ORCID 0000-0001-9476-9539

Mahsa MohagheghSchool of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand.ORCID 0000-0003-2228-8300

Abdolhossein SarrafzadehBatten College of Engineering and Technology, Old Dominion University, Norfolk, VA 23529, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AcousticsMarkov ChainsSignal Processing, Computer-AssistedAlgorithmsAnimalsBeesGenerative Adversarial NetworksGenerative Artificial Intelligencebee bioacousticgenerative adversarial networksMarkov Chainsmart beekeepingsynthetic data

Identifiers

PMID41600168

DOI10.3390/s26020371

PMCPMC12845764

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.