# Multi-Scale Deep Feature Fusion with Machine Learning Classifier for Birdsong Classification
## 3 Experiments and Analysis of Results
### Ablation Study  
#### 3.5.1 The Effectiveness of Multi-Scale Deep Features  
To evaluate the reasons behind the enhancement of birdsong recognition performance brought about by the multi-scale deep features, we conducted a comparative analysis on Dataset A. Table 5 presents the classification results achieved using the KNN method for \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **Method** & **Accuracy (\%)** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{6}{*}{Muti-scale log-power spectrogram deep features} & LR & 94.13\% & 0.9411 & 0.9389 & 0.9395 \\  & KNN & 94.83\% & 0.9513 & 0.9418 & 0.9457 \\  & NB & 92.22\% & 0.9242 & 0.9161 & 0.9184 \\  & DT & 87.36\% & 0.8694 & 0.8681 & 0.8674 \\  & RF & 93.65\% & 0.9383 & 0.9313 & 0.9338 \\  & XGBoost & 93.47\% & 0.9372 & 0.9294 & 0.9323 \\  & CatBoost & 94.27\% & 0.9461 & 0.9390 & 0.9417 \\  & MLP & 94.27\% & 0.9428 & 0.9388 & 0.9400 \\  & SVM & 94.72\% & 0.9499 & 0.9430 & 0.9456 \\ \hline \multirow{6}{*}{Muti-scale log-Mel spectrogram deep features} & LR & 95.31\% & 0.9510 & 0.9481 & 0.9489 \\  & KNN & 95.69\% & 0.9547 & 0.9512 & 0.9522 \\ \cline{1-1}  & NB & 94.27\% & 0.9423 & 0.9364 & 0.9377 \\ \cline{1-1}  & DT & 92.47\% & 0.9205 & 0.9171 & 0.9182 \\ \cline{1-1}  & RF & 94.48\% & 0.9407 & 0.9395 & 0.9385 \\ \cline{1-1}  & XGBoost & 94.48\% & 0.9421 & 0.9374 & 0.9388 \\ \cline{1-1}  & CatBoost & 95.00\% & 0.9474 & 0.9450 & 0.9454 \\ \cline{1-1}  & MLP & 95.21\% & 0.9501 & 0.9467 & 0.9477 \\ \cline{1-1}  & SVM & 95.69\% & 0.9554 & 0.9517 & 0.9527 \\ \hline \multirow{6}{*}{Muti-scale log-Mel spectrogram deep features} & LR & 92.50\% & 0.9241 & 0.9196 & 0.9209 \\  & KNN & 93.58\% & 0.9383 & 0.9265 & 0.9314 \\ \cline{1-1}  & NB & 90.28\% & 0.9001 & 0.8924 & 0.8941 \\ \cline{1-1}  & DT & 85.24\% & 0.8469 & 0.8400 & 0.8413 \\ \cline{1-1}  & RF & 91.32\% & 0.9125 & 0.9011 & 0.9052 \\ \cline{1-1}  & XGBoost & 91.60\% & 0.9161 & 0.9077 & 0.9104 \\ \cline{1-1}  & CatBoost & 92.01\% & 0.9204 & 0.9115 & 0.9145 \\ \cline{1-1}  & MLP & 92.47\% & 0.9240 & 0.9187 & 0.9206 \\ \cline{1-1}  & SVM & 92.71\% & 0.9274 & 0.9193 & 0.9224 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of our methods (multi-scale feature extraction with multiple classifiers) used on Database B. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **Method** & **Accuracy (\%)** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{6}{*}{Muti-scale log-Mel spectrogram deep features} & LR & 96.29\% & 0.9649 & 0.9560 & 0.9601 \\  & KNN & 97.81\% & 0.9790 & 0.9778 & 0.9784 \\  & NB & 92.52\% & 0.9281 & 0.9156 & 0.9172 \\ \cline{1-1}  & DT & 85.97\% & 0.8621 & 0.8524 & 0.8568 \\ \cline{1-1}  & RF & 95.24\% & 0.9553 & 0.9411 & 0.9474 \\ \cline{1-1}  & XGBoost & 95.06\% & 0.9527 & 0.9475 & 0.9499 \\ \cline{1-1}  & CatBoost & 95.67\% & 0.9591 & 0.9498 & 0.9541 \\ \cline{1-1}  & MLP & 96.16\% & 0.9640 & 0.9549 & 0.9591 \\ \cline{1-1}  & SVM & 96.74\% & 0.9696 & 0.9669 & 0.9682 \\ \hline \multirow{6}{*}{Muti-scale log-Mel spectrogram deep features} & LR & 91.59\% & 0.9200 & 0.9048 & 0.9115 \\ \cline{1-1}  & KNN & 94.04\% & 0.9443 & 0.9295 & 0.9360 \\ \cline{1-1}  & NB & 90.88\% & 0.9109 & 0.8988 & 0.9037 \\ \cline{1-1}  & DT & 80.86\% & 0.8049 & 0.7999 & 0.8016 \\ \cline{1-1}  & RF & 90.84\% & 0.9165 & 0.8959 & 0.9048 \\ \cline{1-1}  & XGBoost & 90.36\% & 0.9113 & 0.8877 & 0.8977 \\ \cline{1-1}  & CatBoost & 91.31\% & 0.9177 & 0.9046 & 0.9105 \\ \cline{1-1}  & MLP & 91.63\% & 0.9190 & 0.9082 & 0.9131 \\ \cline{1-1}  & SVM & 91.97\% & 0.9254 & 0.9076 & 0.9154 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._different fusion approaches to multi-scale spectral deep features extracted by ResNet18. For instance, employing the log-Mel spectrum, the single-scale feature method attained an accuracy of 96.89%, whereas a multi-scale configuration with three scales achieved an accuracy of 97.81%.