Wild Bird Species Identification Based on a Lightweight Model With Frequency Dynamic Convolution

Abstract

The sound recognition technology is used to count bird speciespopulation to provide accurate data for population ecology and conservation biology research. Bird species can be identified by spectrogram of bird’s sounds. The existing Convolutional Neural Network(CNN) is not good at mining the relationship between features in time-frequency tasks, and the existing CNN has more parameters and complicated calculation, so existing models are not suitable for deployment in an actual field environment. In order to fill this gap, this paper proposes a lightweight model with frequency dynamic convolution for bird species identification. We use frequency dynamic convolution innovatively to better capture features of bird sounds at different frequencies. First of all, we replaced two-dimensional convolution with frequency-dynamic convolution in order to achieve not-shift invariance of the bird sound’s spectrogram, so that we can effectively capture the feature differences of spectrogram in different frequency bands. Then, we replaced part of the Squeeze and Excitation(SE) attention mechanism with the Coordinate Attention(CA) attention mechanism in order to get more comprehensive global information. Finally, the feature fusion module was used to fuse the local and global features. In addition, we built a datasets containing 160 bird sounds, improving the generalization ability of the model. The experimental results show that our model has good generalization ability and is superior to the existing Lightweight CNN, and obtained better results in top1 accuracy and top5 accuracy.

Keywords

• Deep learning
• bird species identification
• bird sounds recognition
• frequency dynamic convolution
• attention mechanism