Detection of Bird Species Using Acoustic Signals

Abstract

The task at hand is to use supervised learning to determine which bird species are audible in a given recording. In order to extract valuable ecological data from field recordings, it may be necessary to first develop efficient algorithms for classifying bird species. In this study, we use SVM (Support vector machine) technique to categorize bird chipping sounds into their respective species. Memory management, the availability of high-quality bird species for machine recognition, and a difference in signal-to-noise ratio across sets used for training and testing all posed problems for this research. We utilized the SVM technique to solve these problems and found that it provided satisfactory results. In this case, SVM is the most effective technique for dealing with recognizing problems. simply, then CNN tweaking, testing, and categorization. Birds are categorized based on their attributes (size, color, species, etc.) & outcome is contrasted with pre-trained data to provide output. In addition, we use the KNN, LR, and random forest algorithms. In this project, we provide information on many species, including their native territory, diet, name, maximum age, plumage color, body length, and whether or not they are migratory. Hearing who it is on the other end of the line only by hearing their voices. People with low vision may benefit from this endeavor. The concept involves using bird calls to provide early warnings of impending rain.