A Novel Deep Learning Approach for Classification of Bird Sound Using Mel Frequency Cepstral Coefficients

Abstract

Monitoring animal populations is one important matter to better understand changes in their population, behavior, and biodiversity. Bird sounds are the main tool to classify bird species acoustically. The sounds of birds are an indicator for ecologists as it responds to changes in their environment. The recognition among a variety of bird species to get important features is computationally expensive. With the unbalanced classes and scarcity of training data, the performance accuracy is degrading. This paper aims to classify species of birds using lightweight convolutional neural networks (LWCNNs) basis on using a spectrogram image of Brazilian bird sounds as a dataset. For extracting spectrogram images, Mel Frequency Cepstral Coefficient (MFCC) algorithm is used. To prove the high performance of the classifier, ten species of birds with 10,000 spectrogram images are provided to the classifier. Our LWCNN model achieved a training and testing accuracy of 99.68 % and 92.80 % respectively in 10.54 min with 5 epochs.

Keywords

• Bird sound
• Deep Learning
• Light Wight Convolutional Neural Networks
• Classification
• Mel Frequency Cepstral Coefficients