Preventing Deforestation in the Indian Landscape Through Neural Network-Based Intelligence Using Sound Event Detection and Advanced Feature Extraction Techniques

Abstract

Forests play a vital role in maintaining ecological balance, regulating the climate, and conserving biodiversity. However, India’s forest landscape has witnessed significant changes between 1980 and 2024 due to deforestation, afforestation, and evolving conservation strategies. To address the challenges associated with forest monitoring, we proposed a model based on Sound Event Detection using a dataset comprising four classes: chainsaw sounds, handsaw sounds, axe-cutting sounds (synthetic), and negative environmental sounds (e.g., birds, animals, wind). The dataset was constructed from publicly available resources, except for the axe-cutting sound class, which was prepared synthetically. The model employed six feature extraction techniques Mel-Spectrogram, Mel-Frequency Cepstral Coefficients (MFCC), Chroma, Spectral Contrast, Tonnetz, and Spectral Bandwidth to capture critical audio characteristics. These features enabled the efficient representation of harmonic content, temporal patterns, and timbre, which were essential for distinguishing between classes. The proposed approach was executed using various deep learning models, including Customized 1D Convolutional Neural Networks (CNN), Bi-directional Convolutional Recurrent Neural Networks (Bi-CRNN), Bi-directional Gated Recurrent Unit-based CRNNs (Bi-GRU-CRNN), AlexNet, and ResNet. The Customized-CNN, implemented using Keras, demonstrated superior performance with an accuracy of 98%. The model’s effectiveness was further validated as accuracy increased progressively from 95 to 98% when transitioning from two to six feature extraction clusters.