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
[1] Prof. Pooja Wale, Abhishek Mankar, Pratik Padale, Sanket Gawade, Prasanna Ghogare, "A Survey On Bird Species Identification Using Audio Signal Processing And Neural Network," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2022.11210