Species Identification of Birds Via Acoustic Processing Signals Using Recurrent Network Analysis (RNN)

Abstract

Bird watching is a popular pastime, but without the proper identification guides, it may be difficult to tell different species apart. There are more than 9000 recognized bird species. Some bird species are notoriously difficult to predict when they are first identified. Additionally, seeing beliefs when it comes to communicating with birds as a service to birdwatchers, developed a system that uses RNNS to classify bird species. RNNs are an effective machine learning algorithm suite that has shown great promise in the fields of image and audio processing. In this study, investigate potential methods for bird recognition and create a fully automated method for doing so. Automatically identifying bird calls without human intervention is an arduous task that has necessitated much research into the taxonomy and other areas of ornithology. In this study, ID is assessed from two distinct perspectives. The first thing to do was make a complete database of recorded bird calls. Following that, other techniques were used to the sound samples prior to further processing. These included pre-emphasis, framing, quiet eradication, and rebuilding. Each reconstructed audio specimen was given its spectrogram. A neural network was then constructed, trained, and applied to classify the bird species. The consequences of the proposed methodology exhibit that it has been 80% accurate in predicting the identification of bird species.