R-Net: An LSTM-Assisted Deep Learning Framework for the Classification of Bird Species Based on Sound-Spectrogram in Rambutan Agriculture Field

• Theresa Jose,
• J Albert Mayan,

Vol. 19, No. 11, November 30, 2025
10.3837/tiis.2025.11.014, Download Paper (Free):

• avian sound
• Attention Mechanism
• bird sound detection
• Deep Learning
• hyperparameter tuning
• Mel Spectrogram
• SincNet filter

Abstract

Accurate bird species classification is as crucial to monitoring biodiversity and ecological studies as it is in agricultural settings, where birds have both positive and negative impacts on crop production. The proposed research presents a Long Short-Term Memory (LSTM) assisted deep learning framework, RambutanNet (R-Net), designed to classify bird species from their sound spectrograms. R-Net utilises the capabilities of LSTM, integrated with neural networks, to efficiently extract temporal and spatial features from spectrograms of calls. This hybrid model effectively captures both local frequency patterns and long-range dependencies of the audio signals, which is crucial for differentiating between species with similar acoustic signatures. The spectrogram dataset is created by recording sounds from birds using high-fidelity audio equipment and sourced through various repositories. The recordings were pre-processed to remove noise and irrelevant environmental sounds, then converted into mel-spectrograms. The spectral content is fed into the R-Net framework to perform feature extraction and LSTM layers that model the temporal relationships in the audio sequences. The R-Net model was developed and deployed in a Raspberry Pi 5 single-board computer, and its validation and demonstration of performance in multiple fields were conducted. The model achieved an accuracy of 97.86%, along with precision and recall of 98.03% and 96.15%, respectively, showing the extensibility of the model that distinguishes bird species even in extreme natural environments. Another measure is the F1 score, which balances precision with recall and proved to be 98.34%. The substantial AUROC value of 0.95 also points to the model's accurate ability to differentiate among bird species according to their acoustic signatures.

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