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

## TL;DR

This paper introduces a new method for generating realistic bee sounds that can help monitor hive health and address data scarcity in bioacoustics.

## Contribution

The novel MCWaveGAN framework adds a Markov Chain refinement stage to improve the realism of synthesized bee bioacoustic signals.

## Key findings

- MCWaveGAN outperforms WaveGAN in capturing temporal and spectral features of bee bioacoustic signals.
- Synthesized signals improve hive status prediction accuracy when used in classification models.

## 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.