01294nas a2200157   4500008004100000245007800041210006900119260002600188520079200214100001701006700001401023700001801037700001701055700001401072856005001086       2018                        eng d00aClassification of Cetacean Whistles Based on Convolutional Neural Network0 aClassification of Cetacean Whistles Based on Convolutional Neura  aHangzhou, ChinabIEEE3 a<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>
1 aLiu, Songzuo1 aLiu, Meng1 aWang, Mengjia1 aMa, Tianlong1 aQing, Xin  uhttps://ieeexplore.ieee.org/document/8555732/