Deep Feature Fusion of Multi-Dimensional Neural Network for Bird Call Recognition

In order to improve the accuracy of bird sound monitoring during night migration， this paper proposed a deep feature fusion system of multi-dimensional neural network for bird sound classification. Firstly， we proposed an improved VGG Style model， which used log-scaled Mel spectrogram as training feature to enhance the energy distribution of spectrogram， and generate virtual data by mix up to reduce model over-fitting. Then， the pre-trained VGG Style was used to generate deep features for each bird sound. In view of the complementarity of different dimensional models， 1D CNN-LSTM， 2D VGG Style and 3D DenseNet121 were employed as feature extractors to generate advanced features. For 1D CNN-LSTM， in order to obtain richer time-frequency information， the wavelet decomposition was used as pooling method to extract multi-level LBP features from time domain and frequency domain respectively as training input. Meanwhile， the fully connected layer of CNN-LSTM and DenseNet121 were optimized to reduce model parameters and improve real-time performance. Finally， the deep features of three models were fused and fed to K-nearest neighbor for classification， which got the balanced-accuracy of 93.89% for a public dataset CLO-43SD of 5428 flight calls spanning 43 species and exceeded the latest fusion of Mel-VGG and Subnet-CNN by 7.58%.