A Self‐Supervised Approach for Hainan Gibbon Call Separation in Passive Acoustic Monitoring
- 1School of Information Science and Technology Beijing Forestry University Beijing China
- 2Hebei Key Laboratory of Smart National Park Beijing China
Abstract
Passive acoustic monitoring (PAM) provides an effective approach for ecological research and endangered species conservation. However, automated analysis of PAM recordings from the critically endangered Hainan gibbon ( Nomascus hainanus ) remains challenging due to environmental noise and the lack of paired clean reference recordings. Supervised source separation methods often require extensive annotated datasets, limiting their applicability to real‐world ecological recordings. To address these challenges, this study proposes MC‐RemixIT, a self‐supervised source separation approach designed for Hainan gibbon calls under complex acoustic conditions. Building on RemixIT, MC‐RemixIT introduces a lightweight call enhancement module with attention mechanisms, signal cross‐correlation constraints to improve target–noise separation, and multiple training constraints to enhance separation robustness. The proposed approach operates directly on raw waveforms and does not require fully clean reference signals during training. Experimental results demonstrate that MC‐RemixIT consistently improves separation performance compared with RemixIT and bioacoustic denoising frameworks. Moreover, downstream call presence detection experiments show that target‐call estimates generated by MC‐RemixIT improve detection performance across multiple deep learning architectures, indicating enhanced usability of the separated signals for automated acoustic analysis. MC‐RemixIT provides a practical self‐supervised solution for improving wildlife acoustic recordings in noisy environments and supports large‐scale bioacoustic monitoring of endangered species.
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BibTeX
@article{zi2026selfsupervised,
title = {{A Self‐Supervised Approach for Hainan Gibbon Call Separation in Passive Acoustic Monitoring}},
author = {Jiali Zi and Nuo Xu and Yanqi Dong and Yi Xia and Weilin Wang and Feixiang Chen},
journal = {Ecology and Evolution},
year = {2026},
month = oct,
doi = {10.1002/ece3.74542},
url = {https://onlinelibrary.wiley.com/doi/10.1002/ece3.74542},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A Self‐Supervised Approach for Hainan Gibbon Call Separation in Passive Acoustic Monitoring
AU - Jiali Zi
AU - Nuo Xu
AU - Yanqi Dong
AU - Yi Xia
AU - Weilin Wang
AU - Feixiang Chen
PY - 2026
DA - 2026/10/22
JO - Ecology and Evolution
DO - 10.1002/ece3.74542
UR - https://onlinelibrary.wiley.com/doi/10.1002/ece3.74542
AB - Passive acoustic monitoring (PAM) provides an effective approach for ecological research and endangered species conservation. However, automated analysis of PAM recordings from the critically endangered Hainan gibbon ( Nomascus hainanus ) remains challenging due to environmental noise and the lack of paired clean reference recordings. Supervised source separation methods often require extensive annotated datasets, limiting their applicability to real‐world ecological recordings. To address these challenges, this study proposes MC‐RemixIT, a self‐supervised source separation approach designed for Hainan gibbon calls under complex acoustic conditions. Building on RemixIT, MC‐RemixIT introduces a lightweight call enhancement module with attention mechanisms, signal cross‐correlation constraints to improve target–noise separation, and multiple training constraints to enhance separation robustness. The proposed approach operates directly on raw waveforms and does not require fully clean reference signals during training. Experimental results demonstrate that MC‐RemixIT consistently improves separation performance compared with RemixIT and bioacoustic denoising frameworks. Moreover, downstream call presence detection experiments show that target‐call estimates generated by MC‐RemixIT improve detection performance across multiple deep learning architectures, indicating enhanced usability of the separated signals for automated acoustic analysis. MC‐RemixIT provides a practical self‐supervised solution for improving wildlife acoustic recordings in noisy environments and supports large‐scale bioacoustic monitoring of endangered species.
SN - 2045-7758
SN - 2045-7758
ER -