# Multi-Scale Deep Feature Fusion with Machine Learning Classifier for Birdsong Classification
## 3 Experiments and Analysis of Results
### Comparison with State-of-the-Art Methods  
To evaluate the effectiveness of the proposed method, a comparison was made with five state-of-the-art birdsong recognition models, as outlined in Table 7. The results, drawn from the same dataset (Dataset A) used in [20], demonstrate that our method outperforms all competing models, particularly when using log-Mel spectrogram features.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **Model** & **Accuracy** & **Precision** & **Recall** & **\(F1\) Score** \\ \hline Log-power & Resnet50 & 92.07\% & 0.9236 & 0.8962 & 0.9063 \\ spectrogram & CNN & 87.65\% & 0.8797 & 0.8524 & 0.8626 \\ \hline Log-Mel & Resnet50 & 93.39\% & 0.9336 & 0.9274 & 0.9297 \\ spectrogram & CNN & 91.39\% & 0.9131 & 0.8962 & 0.9032 \\ \hline Log-wavelet & Resnet50 & 90.53\% & 0.9101 & 0.8978 & 0.9029 \\ spectrogram & CNN & 83.99\% & 0.8349 & 0.8178 & 0.8240 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification results of other convolutional network using spectrum features.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature Combination** & **Accuracy** \\ \hline Log-power spectrum, single scale (7 \(\times\) 7) & 94.20\% \\ Log-power spectrum, two scales (14 \(\times\) 14 + 7 \(\times\) 7) & 95.78\% \\ Log-power spectrum, multi-scale (28 \(\times\) 28 + 14 \(\times\) 14 + 7 \(\times\) 7) & 95.89\% \\ Log-Mel spectrum, single scale (7 \(\times\) 7) & 96.89\% \\ Log-Mel spectrum, two scales (14 \(\times\) 14 + 7 \(\times\) 7) & 97.63\% \\ Log-Mel spectrum, multi-scale (28 \(\times\) 28 + 14 \(\times\) 14 + 7 \(\times\) 7) & 97.81\% \\ Log-wavelet spectrum, single scale (7 \(\times\) 7) & 92.42\% \\ Log-wavelet spectrum, two scales (14 \(\times\) 14 + 7 \(\times\) 7) & 93.86\% \\ Log-wavelet spectrum, multi-scale (28 \(\times\) 28 + 14 \(\times\) 14 + 7 \(\times\) 7) & 94.04\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy of multi-scale deep features extracted by Resnet18 with KNN Classification.  
Among the state-of-the-art methods, MFF-ScSEnet previously held the highest accuracy at 0.9666, leveraging Mel-Sinc spectrograms for feature extraction. However, the proposed method demonstrates a substantial improvement, attaining an accuracy of 0.9781 with log-Mel spectrogram features. This substantial enhancement underscores the efficacy of the proposed multi-scale feature fusion approach, which adeptly captures the intricate frequency patterns inherent to birdsong recognition.  
The comparison further underscores the superior generalization capability of our method in comparison to hybrid deep learning frameworks, such as (2 DCNN + 3 DCNN) + GRU + GRU, which attained an accuracy of 0.9588, and SFLN (GRU), which achieved an accuracy of 0.9592. While these approaches are effective in processing continuous frame sequences, they appear to be less effective in fully utilizing spectral feature information. In a similar vein, models based on Efficient Net, including EfficientNetV1_b0 and EfficientNetV2_s, demonstrate lower accuracy (0.9502 and 0.9530, respectively), thereby underscoring the efficacy of the proposed framework in leveraging spectral features for birdsong recognition.  
While the proposed approach demonstrates higher accuracy compared to existing methods, further studies and broader evaluations are needed to ascertain its applicability and confirm its generalizability across diverse datasets.