A Unified Deep Supervised Network for Effective Animal Voice Recognition

Hikmat Yar^1,2, Zulfiqar Ahmad Khan³, Samee Ullah Khan⁴, Habib Khan⁵, Sung Wook Baik^1,*
1 Sejong University, Seoul, Republic of Korea
2 KAIST InnoCORE PRISM-AI Center, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
3 Department of Computing Science, Umeå University, Umeå, Sweden
4 Advanced Research and Innovation Centre, Khalifa University, Abu Dhabi, United Arab Emirates
5 Department of Artificial Intelligence, Gachon University, Seongnam-si, Republic of Korea
* Corresponding Author: Sung Wook Baik. Email: email

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.084156

Received 16 April 2026; Accepted 19 August 2026; Published online 07 September 2026

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Abstract

In the realm of animal voice recognition, this work introduces AVRNet, a task-specific integration framework that combines separable convolutions, hierarchical deep supervision through auxiliary classifiers, skip connections, and a dual-pooling channel-spatial attention mechanism configured for spectrogram-based animal vocalization recognition. The combination is designed to provide a favorable trade-off between recognition accuracy and computational cost. Together, these modules enhance the interpretability, training efficiency, and overall performance of the model, making a significant contribution to the development of animal voice recognition technology. In the existing methods for animal voice recognition, researchers used attention mechanisms with average or max-pooling layers in the attention mechanisms. However, average pooling may overlook fine details by focusing on the global context, while max pooling may miss broader patterns by emphasizing only the most prominent features. Therefore, the proposed work incorporates both pooling strategies in the channel and spatial attention mechanisms. To further enhance performance while reducing computational cost, separable convolution is introduced in the spatial attention mechanism. The modified attention mechanisms are incorporated into the animal voice recognition network to capture the intricate nuances of animal sounds, which play a pivotal role in identifying distinct animal species based on vocalizations. To demonstrate the effectiveness of the animal voice recognition network, this work created a novel dataset encompassing a diverse set of different species. The performance of the animal voice recognition network was rigorously evaluated on two datasets: the newly created dataset (containing 4378 samples in 16 classes) and a dataset presented by EmreSasmaz (containing 875 samples in 10 classes). The quantitative and qualitative analysis, along with ablation studies, showed that the proposed model consistently outperformed state-of-the-art methods on both the EmreSasmaz and the newly created datasets. Additionally, the statistical analysis test further confirms the proposed model’s robustness and effectiveness. Thus, the proposed model is promising for applications in biodiversity monitoring, ecological research, and conservation efforts, and has the potential to contribute significantly to different domains in which there is a need for effective and efficient species recognition through vocalizations.