Abstract: an automatic bird species recognition system has been developed and methods for their identification has been investigated. Automatic identification of bird sounds without physical intervention has been a formidable and onerous endeavor for significant research on the taxonomy and various other sub fields of ornithology. In this pa- per, a two-stage identification process is employed. The first stage in- volved construction of an ideal dataset which incorporated all the sound recordings of different bird species. Subsequently, the sound clips were subjected to various sound preprocessing techniques like pre-emphasis, framing, silence removal and reconstruction. Spectrograms were gen- erated for each reconstructed sound clip. The second stage involved deploying a neural network to which the spectrograms were provided as input. Based on the input features, the Convolutional Neural Net- work (CNN) classifies the sound clip and recognizes the bird species.A Real time implementation model was also designed and executed for the above described system.
Keywords: Bird, Computer Vision, Machine Learning, Classification, Neural Network, Self-Learning, Cnn , Audio Signal Processing.
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DOI:
10.17148/IJARCCE.2022.11210
