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
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’ 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.
1 aLiu, Songzuo1 aLiu, Meng1 aWang, Mengjia1 aMa, Tianlong1 aQing, Xin uhttps://ieeexplore.ieee.org/document/8555732/