Visual and Acoustic Identification of Bird Species

Abstract

In our world, there are above 9000 bird species. Some bird species are being found rarely and if found also prediction becomes very difficult. In order to overcome this problem, we have an effective and simple way to recognize these bird species based on their features. This project presents a novel approach for bird species identification that relies on both visual features extracted from unconstrained bird images and acoustic features extracted from bird vocalizations. Since, among most of the approaches we studied Convolutional Neural Networks (CNN) as outperform. So we have used CNN for both visual as well as acoustic identification. CNN’s are the strong assemblage of machine learning which have proven efficient in image processing. Our project draws upon techniques from speech recognition and recent advances in the domain of deep learning. With novel pre-processing and data augmentation methods, we train a convolutional neural network on the biggest publicly available dataset. In our approach, we are using the transfer learning approach for training our neural model. By establishing a dataset and using the algorithm of similarity comparison, this system is proved to achieve good results in practice. By using this method, everyone can easily identify the species of the particular bird which they provide image/audio or both as input to the system.