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This study presents VBSNet, a deep learning model designed to classify the calls of the endangered western black-crested gibbon using passive acoustic monitoring data. The model combines VGG16 for feature extraction, bi-directional LSTM for sequence modeling, and an SE attention mechanism for adaptive feature weighting, achieving an accuracy of 98.35%. This research aims to enhance wildlife conservation efforts by providing a robust method for monitoring biodiversity through automated bioacoustic analysis.

Author

RS Bhayangkara Manado

Language

EN