Bioacoustic Sound Classification Based on Noise and Vocalizations Using Convolutional Neural Networks: A Comprehensive Systematic Review

Year 2025, Volume: 17 Issue: 2, 472 - 484, 30.12.2025

Mirza Farhan Azhari Gibran Maulana Syah Sri Nurdiati Elis Khatizah Mohamad Khoirun Najib

https://doi.org/10.47000/tjmcs.1689564

https://izlik.org/JA92KC44GG

Abstract

The classification of animal vocalizations through bioacoustic analysis has become a crucial tool in wildlife monitoring and conservation. This study presents a Systematic Literature Review (SLR) focused on the use of Convolutional Neural Networks (CNNs) for animal sound classification, synthesizing insights from 21 peer-reviewed journal articles published between 2016 and 2024. The review investigates the effectiveness of CNN models, the impact of different architectures (e.g., ResNet, VGG, MobileNet, Xception), commonly used evaluation metrics, and the advantages and limitations of CNN-based approaches. Results show that CNNs consistently outperform traditional classifiers by leveraging spectrogram-based feature extraction techniques and deep feature learning, achieving high classification accuracy across birds, mammals, amphibians, and marine species. Lightweight CNNs provide viable alternatives for real-time and resource-constrained applications, while ensemble learning and transfer learning further enhance model performance. Nonetheless, challenges persist, including computational cost, interpretability, and domain-specific generalization. This review underscores CNNs' growing role in ecological research and highlights areas for future improvement, particularly in data-scarce environments. The findings contribute to guiding future implementations and innovations in automated animal sound classification systems.

Keywords

deep learning , feature extraction , PRISMA , transfer learning , wavelet

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