A comprehensive Bird ecosystem monitorization and analysis tool from sound recognition using Machine Learning

Abstract

This project aims to develop a system for bird identification based on their songs, targeting ecosystem researchers, ornithologists, and nature enthusiasts. The system integrates the BirdNet model to process bird audio for continuous monitorization. It incorporates automatic mechanisms to record the audio from a capture device, pass it to the Machine Learning model, and store the data for later access and analysis. The system features a graphical interface via a web application, enhancing the user experience and presenting data in a clear format. The interface displays analyses in a table format with dates and times, and it offers dynamic statistics and charts that update with new analyses and data. This application can be scaled to contribute to the creation of a distributed system that collects data from broader ecosystems. As it is based on a widely used machine learning model, it can be integrated with other utilities that make use of this model.