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Ved Chaudhari, Siddharth Chhajed, Piyush Jain, Adarsh Kamble, Dnyaneshwar Kapse,
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- Student,, Professor,, Student,, Student,, Student, Rajiv Gandhi Institute of Technology, Kharghar, Navi Mumbai,, Rajiv Gandhi Institute of Technology, Kharghar, Navi Mumbai,, Rajiv Gandhi Institute of Technology, Kharghar, Navi Mumbai,, Rajiv Gandhi Institute of Technology, Kharghar, Navi Mumbai,, Rajiv Gandhi Institute of Technology, Kharghar, Navi Mumbai, Maharashtra,, Maharashtra,, Maharashtra,, Maharashtra,, Maharashtra, India, India, India, India, India
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Abstract
nBird 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
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Keywords: Ornithology, CNN, Spectogram, Bird species recognition, Machine learning.
n[if 424 equals=”Regular Issue”][This article belongs to Journal of Aerospace Engineering & Technology(joaet)]
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References
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- Á. Incze, H. -B. Jancsó, Z. Szilágyi, A. Farkas and C. Sulyok, “Bird Sound Recognition Using a Convolutional Neural Network,” 2018 IEEE 16th International Symposium on Intelligent Systems and Informatics (SISY), Subotica, Serbia, 2018, pp. 000295-000300, doi: 10.1109/SISY.2018.8524677.
- Chandu, A. Munikoti, K. S. Murthy, G. Murthy V. and C. Nagaraj, “Automated Bird Species Identification using Audio Signal Processing and Neural Networks,” 2020 International Conference on Artificial Intelligenceand Signal Processing (AISP), Amaravati,India, 2020, pp. 1-5, doi: 10.1109/AISP48273.2020.9073584.
- C. Shekar, C. B. L., “Birds Audio Detection using Convolutional Neural Network and Transfer Learning,” in IEEE Transactions on Audio and Speech Processing, vol. 08, no. 09, pp. 2021, Sep. 2021.
- Lasseck, “Acoustic Bird Detection with Deep Convolutional Neural Networks,” in Detection and Classification of Acoustic Scenes and Events 2018 Challenge, Technical Report, 2018.
- Sprengel, M. Jaggi, Y. Kilcher, and T. Hofmann, “Audio based bird species identification using deep learning techniques,” Working notes of CLEF, 2016.
- Lasseck, “Audio-based Bird Species Identification with Deep Convolutional Neural Networks,” in Proceedings of the Working Notes of CLEF 2018, Museum für Naturkunde – Leibniz Institute for Research on Evolution and Biodiversity.
- Kahl, T. Wilhelm-Stein, H. Hussein, H. Klinck, D.Kowerko, M. Ritter, and M. Eibl, “Large-scale bird sound classification using convolutional neural networks,” Working notes of CLEF, 2017.
- Fazekas B, Schindler A, Lidy T (2017) A Multi -modal Deep Neural Network approach to Bird -song Identification. In: Working Notes of CLEF 2017.
- Ruff ZJ, Lesmeister DB, Duchac LS, Padmaraju BK, Sullivan CM. Automated identification of avian vocalizations with deep convolutional neural networks. Remote Sensing in Ecology and Conservation. 2020 Mar;6(1):79-92.
- LeBien J, Zhong M, Campos-Cerqueira M, Velev JP, Dodhia R, Ferres JL, Aide TM. A pipeline for identification of bird and frog species in tropical soundscape recordings using a convolutional neural network. Ecological Informatics. 2020 Sep 1; 59:101113.
- Jasim HA, Ahmed SR, Ibrahim AA, Duru AD. Classify bird species audio by augment convolutional neural network. In2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) 2022 Jun 9 (pp. 1-6). IEEE.
- Grill T, Schlüter J. Two convolutional neural networks for bird detection in audio signals. In2017 25th European Signal Processing Conference (EUSIPCO) 2017 Aug 28 (pp. 1764-1768). IEEE.
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| Volume | 14 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 02 | |
| Received | July 29, 2024 | |
| Accepted | July 31, 2024 | |
| Published | August 16, 2024 |
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