Abstract
In the past two decades, a plethora of efforts have been given to the field of automatic classification of bird sounds, which can facilitate a long-term, non-human, and low-energy consumption ubiquitous computing system for monitoring the nature reserve. Nevertheless, human hand-crafted features need numerous domain knowledge, and inevitably make the designing progress time-consuming and expensive. To this line, we propose a sequence-to-sequence deep learning approach for extracting the higher representations automatically from bird sounds without any human expert knowledge. First, we transform the birds sound audio into spectrograms. Subsequently, higher representations were learnt by an autoencoder-based encoder-decoder paradigm combined with the deep recurrent neural networks. Finally, two typical machine learning models are selected to predict the classes, i.e., support vector machines and multi-layer perceptrons. Experimental results demonstrate the effectiveness of the method proposed, which can reach an unweighted average recall (UAR) at 66.8% in recognising 86 species of birds.
This work was partially supported by the National Natural Science Foundation of China (Grant No. 61702370), P. R. China, the Key Program of the Natural Science Foundation of Tianjin (Grant No. 18JCZDJC36300), P. R. China, the Open Projects Program of the National Laboratory of Pattern Recognition, P. R. China, the Zhejiang Lab’s International Talent Fund for Young Professionals (Project HANAMI), P. R. China, the JSPS Postdoctoral Fellowship for Research in Japan (ID No. P19081) from the Japan Society for the Promotion of Science (JSPS), Japan, and the Grants-in-Aid for Scientific Research (No. 19F19081) from the Ministry of Education, Culture, Sports, Science and Technology, Japan.
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References
Qian, K., Zhang, Z., Ringeval, F., Schuller, B.: Bird sounds classification by large scale acoustic features and extreme learning machine. In: Proceedings of GlobalSIP, Orlando, Florida, USA, pp. 1317–1321. IEEE (2015)
Qian, K., Guo, J., Ishida, K., Matsuoka, S.: Fast recognition of bird sounds using extreme learning machines. IEEE Trans. Electr. Electron. Eng. 12(2), 294–296 (2017)
Papadopoulos, T., Roberts, S.J., Willis, K.J.: Automated bird sound recognition in realistic settings (2018)
Kaewtip, K.: Robust automatic recognition of birdsongs and human speech: a template-based approach. Ph.D. thesis, UCLA (2017)
Bang, A.V., Rege, P.P.: Evaluation of various feature sets and feature selection towards automatic recognition of bird species. Int. J. Comput. Appl. Technol. 56(3), 172–184 (2017)
Piczak, K.J.: Recognizing bird species in audio recordings using deep convolutional neural networks. In: Proceedings of International Conference on Genetic & Evolutionary Computing, Fujian, China, pp. 534–543. IEEE (2016)
Xie, J., Hu, K., Zhu, M., Yu, J., Zhu, Q.: Investigation of different CNN-based models for improved bird sound classification. IEEE Access 7(8922774), 175353–175361 (2019)
Jia, Y., et al.: Direct speech-to-speech translation with a sequence-to-sequence model. In: Proceedings of Interspeech, Graz, Austria, pp. 1–5. ISCA (2019)
Okamoto, T., Toda, T., Shiga, Y., Kawai, H.: Real-time neural text-to-speech with sequence-to-sequence acoustic model and WaveGlow or single Gaussian WaveRNN vocoders. In: Proceedings of Interspeech, Graz, Austria, pp. 1308–1312. ISCA (2019)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of EMNLP, Doha, Qatar, pp. 1724–1734. Association for Computational Linguistics (2014)
Cho, K., Van Merrinboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. In: Proceedings of SSST-8, Doha, Qatar, pp. 103–111 Association for Computational Linguistics (2014)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Proceedings of Proceedings of the 27th International Conference on Neural Information Processing Systems, Montreal, Canada, pp. 3104–3112. MIT Press (2014)
Qian, K.: Automatic general audio signal classification. Ph.D. thesis, Munich, Germany (2018). Doctoral thesis
Amiriparian, S., Freitag, M., Cummins, N., Schuller, B.: Sequence to sequence autoencoders for unsupervised representation learning from audio. In: Proceedings of the DCASE 2017 Workshop, Munich, Germany, pp. 17–21. IEEE (2017)
Freitag, M., Amiriparian, S., Pugachevskiy, S., Cummins, N., Schuller, B.: auDeep: unsupervised learning of representations from audio with deep recurrent neural networks. J. Mach. Learn. Res. 18(1), 6340–6344 (2017)
Deng, Y., Wang, L., Jia, H., Tong, X., Li, F.: A sequence-to-sequence deep learning architecture based on bidirectional GRU for type recognition and time location of combined power quality disturbance. IEEE Trans. Industr. Inf. 15(8), 4481–4493 (2019)
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
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Qiao, Y., Qian, K., Zhao, Z. (2020). Learning Higher Representations from Bioacoustics: A Sequence-to-Sequence Deep Learning Approach for Bird Sound Classification. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1333. Springer, Cham. https://doi.org/10.1007/978-3-030-63823-8_16
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