Communication signal analysis with fusion classification of captive bottlenose dolphins

Aiming at the problem that the classification accuracy of bottlenose dolphin communication signals（whistle）is reduced due to the mixing of a large number of echolocation signals（click）,a fusion classification method based on machine learning is proposed.The time-frequency distribution features of whistle signals are extracted to train the random forest classifier,and the Mel time-frequency diagram features are used to train the convolution neural network classifier.On this basis,a fusion decision maker is designed to classify and recognize the aliased whistle signals.The classification and recognition results of the experimental data collected from the sound signals of captive dolphins show that the fusion classification method has better classification performance.The classification accuracy of the aliased whistle signals is more than 94%,which is better than the time-frequency distribution feature classifier and Mel timefrequency graph feature classifier,and can improve the classification ability of the aliased signals.