A Multi-Scale Feature Fusion Hybrid Convolution Attention Model for Birdsong Recognition

Birdsong is a valuable indicator of rich biodiversity and ecological significance. Although feature extraction has demonstrated satisfactory performance in classification, single-scale feature extraction methods may not fully capture the complexity of birdsong, potentially leading to suboptimal classification outcomes. The integration of multi-scale feature extraction and fusion enables the model to better handle scale variations, thereby enhancing its adaptability across different scales. To address this issue, we propose a Multi-Scale Hybird Convolutional Attention Mechanism Model (MUSCA). This method combines depth wise separable convolution and traditional convolution for feature extraction and incorporates self-attention and spatial attention mechanisms to refine spatial and channel features, thereby improving the effectiveness of multi-scale feature extraction. To further enhance multi-scale feature fusion, we have developed a layer-by-layer aligned feature fusion method that establishes deeper correlations, thereby improving classification accuracy and robustness. In our study, we investigated the songs of 20 bird species, extracting wavelet spectrogram, log-Mel spectrogram and log-spectrogram features. The classification accuracies achieved by our proposed method were 93.79%, 96.97% and 95.44% for these respective features. The results indicate that the birdcall recognition method proposed in this paper outperforms recent and state-of-the-art methods.