Bird Species Identification using Audio Signal Processing and Neural Networks

Abstract: In this work, automatic bird species recognition systems were developed, and their identification methods were investigated. Automatically identifying bird calls without physical intervention has been a large and tedious endeavor for major studies in various subfields of taxonomy and other ornithology. This task uses a two-step identification process. In the first phase, an ideal dataset was created containing all recordings of different bird species. Next, the sound clip was subjected to various sound pre-processing techniques such as pre-emphasis, framing, silence removal, and reconstruction. A spectrogram was generated for each reconstructed sound clip. The second step used a neural network given with the spectrogram as input. A convolutional neural network (CNN) classifies sound clips and recognizes bird species based on input characteristics. In the above system, a real-time implementation model was also designed and executed. Keywords: Bird species identification, bird sound, sound pre-processing techniques, Convolutional Neural Network, Spectrograms