Classification of Elephant Sounds Using Parallel Convolutional Neural Network

Abstract

Human-elephant conflict is the most common problem across elephant habitat Zones across the world. Human elephant conflict (HEC) is due to the migration of elephants from their living habitat to the residential areas of humans in search of water and food. One of the important techniques used to track the movements of elephants is based on the detection of Elephant Voice. Our previous work [] on Elephant Voice Detection to avoid HEC was based on Feature set Extraction using Support Vector Machine (SVM). This research article is an improved continuum of the previous method using Deep learning techniques. The current article proposes a competent approach to classify Elephant voice using Vocal set features based on Convolutional Neural Network (CNN). The proposed Methodology passes the voice feature sets to the Multi input layers that are connected to parallel convolution layers. Evaluation metrics like sensitivity, accuracy, precision, specificity, execution Time and F1 score are computed for evaluation of system performance along with the baseline features such as Shimmer and Jitter. A comparison of the proposed Deep learning methodology with that of a simple CNN-based method shows that the proposed methodology provides better performance, as the deep features are learnt from each feature set through parallel Convolution layers. The accuracy 0.962 obtained by the proposed method is observed to be better compared to Simple CNN with less computation time of 11.89 seconds.