Sound Classification for Javanese Eagle Based on Improved Mel-Frequency Cepstral Coefficients and Deep Convolutional Neural Network

Abstract

The Javanese Eagle is a rare and protected animal in Indonesia. These animals only live in a few species and are threatened with extinction. These birds need to be bred to avoid extinction. One form of communication between the Javanese eagles and each other is the sound of their tweets. These tweets can be studied and classified to conserve endangered animals. This study will classify the sound of the Javanese Eagles for the benefit of animal conservation. Data in the form of voice tweets will be classified. This classification uses algorithms from improved MFCC (Mel-Frequency Cepstral Coefficients) and Deep Convolutional Neural Network. The result of this study was to classify the sound of the Javanese Eagle from the lack of food or drink, the normal tweets state of the bird, and to find out the Javanese Eagle in finding a partner. This research has been carried out by comparing the CNN architecture with AlexNet and VGGNet models and various combinations of training, validation, and test data. The best model dataset underwent division into 80% for training, 10% for validation, and 10% for testing. Training and testing of both IMFCC and VGGNet models occurred using the same dataset. During training, VGGNet achieved 100% accuracy, while testing yielded 99%. ROC Curve: 'Normal' AUC 0.996, 'Looking for Partner' AUC 1.000, 'Looking for Food' AUC 0.996. This study aids Javanese Eagle conservation, crucial for preventing extinction at conservation sites.