Author: |
Rodiatul Adawiya Abdul Rahman Rodiatul Adawiya Abdul Rahman |
---|---|
Thesis Title: |
Mobile Application for Real-Time Bird Sound Recognition using Convolutional Neural Network Mobile Application for Real-Time Bird Sound Recognition using Convolutional Neural Network |
Advisor: |
楊傳凱
Yang, Chuan-Kai |
Committee: |
賴源正
Yuan-Cheng Lai 林伯慎 Bor-Shen Lin |
Degree: |
碩士 Master |
Department: |
管理學院 - 資訊管理系 Department of Information Management |
Thesis Publication Year: | 2020 |
Graduation Academic Year: | 108 |
Language: | 英文 |
Pages: | 53 |
Keywords (in Chinese): | Audio Features 、Bioacoustics 、Bird Sound Recognition 、Convolutional Neural Networks 、Mobile-based Application |
Keywords (in other languages): | Audio Features, Bioacoustics, Bird Sound Recognition, Convolutional Neural Networks, Mobile-based Application |
Reference times: | Clicks: 419 Downloads: 0 |
Share: |
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[1] Potamitis, I. “Unsupervised dictionary extraction of bird vocalisations and new tools on assessing and visualising bird activity”. Eco. Inform. 26, Part 3, 6–17, 2015.
[2] Stastny, J., Munk, M., Juranek, L. “Automatic bird species recognition based on bird’s vocalization”. EURASIP Journal on Audio, Speech, and Music Processing, 2018.
[3] Dong, X., Towsey, M., Truskinger, A., Cottman-Fields, M., Zhang, J., Roe, P. “Similarity-based birdcall retrieval from environmental audio”. Eco. Inform. 29, Part 1, 66–76, 2015.
[4] Albornoz, E.M, Vignolo, L.D, Sarquis, J.A, Leon, E. “Automatic classification of Furnariidae species from the Paranaense Littoral region using speech-related features and machine learning”. Ecological Informatics 38, 39–49, 2017.
[5] Sprengel, E., Martin Jaggi, Y. K., & Hofmann, T. “Audio based bird species identification using deep learning techniques”. Working notes of CLEF, 2016.
[6] Kontas, M. “Sound-Based Bird Classification: How a group of Polish women used deep learning, acoustics and ornithology to classify birds”. (2020, April 27). Retrieved from Toward Data Science: https://towardsdatascience.com/sound-based-bird-classification-965d0ecacb2b
[7] Kahl, et.al. “Large-Scale Bird Sound Classification using Convolutional Neural Networks”. Computer Science-Published in CLEF, 2017.
[8] Yeo, C.Y, Al-Haddad, S.A.R, Ng, C.K. “Animal Voice Recognition for Identification (ID) Detection System”. IEEE 7th International Colloquium on Signal Processing and its Applications, 2011.
[9] Moscow Zoo, “Gallery of animals’ sounds,” http://www.moscowzoo.ru/get.asp?id=C130, Accessed on May 28, 2020
[10] Bang, A.V, Rege, P. P. “Recognition of Bird Species from their Sounds using Data Reduction Techniques”. ICCCT-2017: Proceedings of the 7th International Conference on Computer and Communication Technology, 2017.
[11] Kaminska, D., Gmerek, A. “Automatic identification of bird species: A comparison between KNN and SOM classifiers”. IEEE Joint Conference New Trends in Audio & Video and Signal Processing: Algorithms, Architectures, Arrangements and Applications, 2012.
[12] Cai, J., Ee, D., Pham, B., Roe, P. and Zhang, J. “Sensor network for the monitoring of ecosystem: Bird species recognition”. IEEE 3rd International Conference on Intelligent Sensors, Sensor Networks and Information (ISSNIP 2007), pp. 293-298, 2007.
[13] Kun, Q., Zixing, Z., Ringeval, F., Schuller, B. “Bird Sounds Classification by Large Scale Acoustic Features and Extreme Learning Machine”. IEEE Global Conference on Signal and Information Processing, 2015.
[14] Tóth, B.P, Czeba, B. “Convolutional Neural Networks for Large-Scale Bird Song Classification in Noisy Environment”. Computer Science-Published in CLEF, 2016.
[15] Narasimhan, R., Fern, X. Z., Raich, R. “Simultaneous segmentation and classification of bird song using CNN”. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017.
[16] Koh, C.Y, et al. “Bird Sound Classification using Convolutional Neural Networks”. Working Notes of CLEF 2019, 2019.
[17] Hinton, G., et al. “Deep neural networks for acoustic modelling in speech recognition: The shared views of four research groups”. IEEE Signal Processing Magazine, 29(6), pp.82-97, 2012.
[18] Müller, L., Marti, M. “Bird sound classification using a bidirectional LSTM”. Working Notes of CLEF 2018 (Cross Language Evaluation Forum), 2018.
[19] Russakovsky, O., et all. “Imagenet large scale visual recognition challenge”. International Journal of Computer Vision, 115(3), 211-252, 2015.
[20] Krizhevsky, A., Sutskever, I., Hinton, G. E. “Imagenet classification with deep convolutional neural networks”. Advances in neural information processing systems, pp. 1097-1105, 2012.
[21] Ioffe, S., Christian, S. “Batch normalization: Accelerating deep network training by reducing internal covariate shift.” arXiv preprint arXiv:1502.03167, 2015.
[22] Nair, V., Hinton, G. E. “Rectified linear units improve restricted boltzmann machines”. Proc. 27th International Conference on Machine Learning, pp. 807-814, 2010.
[23] Pham, A. T., Fern, X. Z., Raich, R. “Dynamic programming for instance annotation in multi-instance multi-label learning”. IEEE Trans. on PAMI, 2014.
[24] Zhou, Z. H., Zhang, M. L., Huang, S. J., Li, Y. F. “Multi-instance multi-label learning,” Artificial Intelligence, pp. 2291–2320, 2012.
[25] He, K., Zhang, X., Ren, S., Sun, J. “Deep residual learning for image recognition”. Proc. IEEE Conf. Computer Vision and Pattern Recognition. pp. 770–778, 2016.
[26] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z. “Rethinking the inception architecture for computer vision”. Proc. IEEE Conf. Computer Vision and Pattern Recognition. pp. 2818–2826, 2016.
[27] Kingma, D., Ba, J. “Adam: A method for stochastic optimization”. arXiv preprint, arXiv:1412.6980, 2014.
[28] Lasseck, M. “Bird song classification in field recordings: winning solution for NIPS4B 2013 competition”. Proc. Int. Symp. Neural Information Scaled for Bioacoustics, sabiod.org/nips4b, joint to NIPS, Nevada. pp. 176–181, 2013.