Common Bird Sound Recognition at Vietnam Based on CNN
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Abstract
This article about developing a software extracting bird sound from a website [13], that has sounds of different bird species in Vietnam, explores the CNN model to develop a bird sound recognition system. The process includes conducting methodological experiments on self-collected datasets, providing assessments based on obtained results and building a bird sound recognition application.
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Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (2017) 2-7
A. Goh, E. Tan, and R. Go, "Recurrent neural networks for bird sound classification," in Proceedings of the 17th International Society for Music Information Retrieval Conference, 2016, pp. 518-524.
A. Dehghani, M. R. Jahromi, and S. A. Monadjemi, "Automatic bird sound classification using recurrent neural network," in Proceedings of the IEEE 2nd International Conference on Applied Robotics for the Power Industry, 2017, pp. 1-6.
Y. Xu, H. Liu, H. Zhang, and Y. Yang, "Bird sound recognition based on CRNN and segment-based data augmentation," in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2019, pp. 451-455.
Franc¸ois Chollet: Xception: Deep Learning with Depthwise Separable Convolutions (2017). https://doi.org/10.1109/CVPR.2017.195
K. J. Piczak, "Environmental sound classification with convolutional neural networks," in Proceedings of the IEEE 25th International Workshop on Machine Learning for Signal Processing, 2015, pp. 1-6. https://doi.org/10.1109/MLSP.2015.7324337
K. J. Piczak, "Environmental sound classification with convolutional neural networks," in Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2015, pp. 1-6. https://doi.org/10.1109/MLSP.2015.7324337
R. R., & Sengupta, A. (2020). Deep learning for bird species classification and sound localization using mel-spectrogram representations. Applied Acoustics, 165, 107355.
MingxingTan, Quoc V. Le: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (2020).
R. Tsai, C. Liao, and S. Lee, "Automatic bird sound recognition using convolutional neural networks," Applied Sciences, vol. 7, no. 3, pp. 280-295, 2017.
Xuedong Huang, Alex cero, Hsiao_wuen Hon (2001). Spoken language processing: A guide to theory, algorithm, and system development. Prentice Hall
Y. Li, F. Xu, J. Zhu, and H. Li, "Deep convolutional neural networks for bird species classification and detection," in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2016, pp. 141-14
https://towardsdatascience.com/sound-based-bird-classification-965d0ecacb2b
Xeno-canto: https://xeno-canto.org
Yogapriya*, J., Dhivya, S., & Suvitha, K. (2020). Convolutional Neural Networks in Image Retrieval System. In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 5, pp. 123–129). https://doi.org/10.35940/ijitee.d2001.039520
Sundararajan*, R. K., S, S., S, V., & Pandian M, J. (2019). Convolutional Neural Network Based Medical Image Classifier. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 3, pp. 4494–4499). https://doi.org/10.35940/ijrte.c6810.098319
Kumar, P., & Rawat, S. (2019). Implementing Convolutional Neural Networks for Simple Image Classification. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 2, pp. 3616–3619). https://doi.org/10.35940/ijeat.b3279.129219
Das, S., S, S., M, A., & Jayaram, S. (2021). Deep Learning Convolutional Neural Network for Defect Identification and Classification in Woven Fabric. In Indian Journal of Artificial Intelligence and Neural Networking (Vol. 1, Issue 2, pp. 9–13). https://doi.org/10.54105/ijainn.b1011.041221