Open Access
Ved Chaudhari,
Siddharth Chhajed,
Piyush Jain,
Adarsh Kamble,
Dnyaneshwar Kapse,
- Student, Rajiv Gandhi Institute of Technology, Kharghar, Navi Mumbai, Maharashtra, India
- Professor, Rajiv Gandhi Institute of Technology, Kharghar, Navi Mumbai, Maharashtra, India
- Student, Rajiv Gandhi Institute of Technology, Kharghar, Navi Mumbai, Maharashtra, India
- Student, Rajiv Gandhi Institute of Technology, Kharghar, Navi Mumbai, Maharashtra, India
- Student, Rajiv Gandhi Institute of Technology, Kharghar, Navi Mumbai, Maharashtra, India
Abstract
Bird species identification is a complex task within ornithology that demands advanced technological solutions. This research presents an approach leveraging Convolutional Neural Networks (CNNs) for bird species recognition based on identification of bird sound, each employing unique datasets and methodologies. The objective involves a two-stage identification process, beginning with the construction of an ideal dataset. The crucial step involves converting 1D audio waveforms to 2D spectrograms, enhancing CNNs’ ability to analyze temporal and frequency features simultaneously. Spectrograms are generated for each sound clip to capture essential features, contributing to advancements in accurate and effective automatic bird species classification in ornithology. In the next step, a neural network, specifically a Convolutional Neural Network (CNN), processed spectrograms as input. CNN analyzed these features, conducting classification on the sound clip and accurately recognizing the bird species associated with the input audio. This underscores CNN’s adeptness in discerning intricate patterns within spectrograms. Our website analyzes user-input audio recordings to predict bird species. Results, displaying identified bird species names and corresponding spectrograms, demonstrate the practical application of the automated recognition system
Keywords: Ornithology, CNN, Spectogram, Bird species recognition, Machine learning.
[This article belongs to Journal of Aerospace Engineering & Technology(joaet)]
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Journal of Aerospace Engineering & Technology
| Volume | 14 |
| Issue | 02 |
| Received | July 29, 2024 |
| Accepted | July 31, 2024 |
| Published | August 16, 2024 |