Abstract:

【Objective】This study aimed to build a birdsong classification model with strong generalization integrating multi-view features and maximizing feature information to promote profound research on bird species diversity protection and ecological environmentally-intelligent monitoring.【Method】Using 16 types of birdsong audio data as the research objects, the short-time Fourier transform (STFT), wavelet transform (WT) and Hilbert-Huang transform (HHT) feature extraction methods were used to generate three types of birdsong spectrograms to constitute multi-view feature data, and as the input of the convolutional neural network (CNN), the base classifiers STFT-CNN, WT-CNN, and HHT-CNN for different views were trained. The multi-view bagging ensemble convolutional neural network (MVB-CNN) and multi-view stacking ensemble convolutional neural network (MVS-CNN) models were constructed using bagging and stacking integration methods, respectively. With the powerful feature extraction capability of CNN, the multi-view cascaded ensemble convolutional neural network (MVC-CNN) model was proposed to cascade and fuse the deep features extracted from different views through CNN. The classification results were obtained by using a support vector machine (SVM). 【Result】The accuracy rates of the base classification models WT-CNN, STFT-CNN, and HHT-CNN constructed in this study were 89.11%, 88.36%, and 81.00%, respectively; the accuracy rates of the ensemble models MVB-CNN and MVS-CNN were 89.92% and 93.54%, respectively; and the accuracy of the multi-view cascade ensemble model MVC-CNN was 95.76%. The accuracy of the MVC-CNN model improved by 6.65%-14.76% over the single-view-based classification model and by 5.84% and 2.22% over the MVB-CNN and MVS-CNN models, respectively.【Conclusion】The MVC-CNN model proposed in this study fully combined the advantages of multi-view features of birdsong, effectively improving the birdsong classification effects with a greater stability and better generalizational ability, providing a technical solution for multi-view birdsong classification researches.

Key words: feature extraction, multi-view, ensemble learning, convolutional neural network