Detecting Bird and Frog Species Using Tropical Soundscape

Abstract

Abstract: In the tropical jungle, hearing a species is considerably simpler than seeing it. The sounds of many birds and frogs may be heard if we are in the woods, but the bird cannot be seen. It is difficult in this these circumstances for the expert in identifying the many types of insects and harmful species that may be found in the wild. An audio-input model has been developed in this study. Intelligent signal processing is used to extract patterns and characteristics from the audio signal, and the output is used to identify the species. Sound of the birds and frogs vary according to their species in the tropical environment. In this research we have developed a deep learning model, this model enhances the process of recognizing the bird and frog species based on the audio features. The model achieved a high level of accuracy in recognizing the birds and the frog species. The Resnet model which includes block of simple and convolution neural network is effective in recognizing the birds and frog species using the sound of the animal. Above 90 percent of accuracy is achieved for this classification task. Keywords: Bird Frog Detection, Neural Network, Resnet, CNN.