SDFIE-NET – A self-learning dual-feature fusion information capture expression method for birdsong recognition

Zhang, Qin; Hu, Shipeng; Tang, Lu; Deng, Rui; Yang, Choujun; Zhou, Guoxiong; Chen, Aibin

doi:10.1016/j.apacoust.2024.110004

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2024

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Cited by 2 publications

References 39 publications

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Orchard bird song recognition based on multi-view multi-level contrastive learning

Wu,

Zhang,

Zheng

et al. 2024

Applied Acoustics

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Orchard bird song recognition based on multi-view multi-level contrastive learning

Wu,

Zhang,

Zheng

et al. 2024

Applied Acoustics

View full text Add to dashboard Cite

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

Li,

Lv,

et al. 2024

Preprint

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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.

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SDFIE-NET – A self-learning dual-feature fusion information capture expression method for birdsong recognition

Cited by 2 publications

References 39 publications

Orchard bird song recognition based on multi-view multi-level contrastive learning

Orchard bird song recognition based on multi-view multi-level contrastive learning

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

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