Detection of Bird and Frog Species from Audio Dataset Using Deep Learning

Abstract

There are over 9000 bird and frog species in the globe. Some of the species are rare to find, and even when they are, predicting their behaviour is challenging. There is an efficient and simple technique to recognise these frog and bird species contingent on their traits to solve this challenge. Also, humans are better at recognising birds and frogs through sounds than they are at recognising them through photographs. As a result, employed various CNN models including CNN-Sequential, CNN-ResNet, CNN-EfficientNet, CNN-VGG19 and a hybrid model Convolution Neural Networks with Long Short-term Memory (CNN-LSTM). It is a powerful deep learning model that has shown to be effective in image processing. Compared to standard alone models, hybrid model produces better accuracy. A hybrid system for classifying bird and frog species is provided in this study, which employs the Rainforest Connection Species Audio Detection dataset from Kaggle repository for both training and testing. The classification of bird or frog species by using audio dataset after processing it and convert it into spectrogram images. Among all the deployed models CNN-LSTM system has been shown to achieve satisfactory results in practise by building this dataset and achieves accuracy of 92.47.