@conference {53448,
	title = {Classification of Cetacean Whistles Based on Convolutional Neural Network},
	booktitle = {2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)},
	year = {2018},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Hangzhou, China},
	abstract = {<p>Vocal communication is a primary communication method of cetaceans. Learning the language of them is of great significance for the protection of cetaceans, and it also provides support for acoustic study of cetaceans\&rsquo; biological behavior. In this study, a classification method based on deep learning is proposed for the classification of cetacean whistles. Firstly, the method performs short-time Fourier transform on whistles to obtain the time-frequency distribution, and then uses the deep learning model, convolutional neural network, to classify the signal based on time-frequency characteristics. The simulation results show that this method has a relatively high classification accuracy when choosing the appropriate parameters, indicating the method is effective.</p>
},
	doi = {10.1109/WCSP.2018.8555732},
	url = {https://ieeexplore.ieee.org/document/8555732/},
	author = {Liu, Songzuo and Liu, Meng and Wang, Mengjia and Ma, Tianlong and Qing, Xin}
}
