Automatic Identification Of Birds Using Speech Signal Processing And Neural Networks

The ecosystem might suffer irreparable damage if we don't keep an eye on the impacts of our actions. Monitoring biodiversity, population dynamics, and animal breeding behaviour is one way to keep an eye on these consequences. Because they are the most vulnerable to environmental changes like deforestation and forest fires, birds are among the best creatures to study. An estimated 1,370 bird species, or around 13% of all bird species, are under threat of going extinct. Despite their ubiquity, many species of birds elude human identification. Up until recently, professionals had to manually monitor the birds, which was both inefficient and time-consuming.To help ecologists with this problem, we suggest using deep learning to determine the species of a bird from an audio clip. In order to do this, we want to employ the most recent Artificial Neural Network (ANN) model for automatic identification of bird species using audio inputs. Our goal in this effort was to develop a more accurate method of classifying bird species. A 100% training accuracy and a 97% validation accuracy are displayed in this data. Since ANN is able to identify the bird species with high accuracy, we can say that it is superior to the current implementation approach.