## 4 Experiments
### Few-Shot ASC Performance  
Few-shot ASC was performed in typical settings such as 5-way 1-shot classification and 5-way 5-shot classification. As an evaluation metric, we used accuracy, which represents the ratio of the number of correct samples to the total number of samples. Tables 3 and 4 present the accuracy comparison results of the learning schemes for the BirdVox-14 SD dataset and the ANAFCC dataset, respectively. Here, the accuracy values represent the mean and 95% confidence interval values for ten repeated experiments. The tables show that TAPT achieves the best accuracy compared to other learning schemes. In addition, Figures 3 and 4 compare the accuracy of the learning schemes on the two backbone networks for the BirdVox-14 SD dataset and the ANAFCC dataset, respectively. From the figures, it can be seen that all learning schemes show better accuracy on VGG11 in most cases due to the depth and number of parameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Learning Scheme**} & \multicolumn{2}{c}{**4 conv**} & \multicolumn{2}{c}{**VGG11**} \\ \cline{2-5}  & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** \\ \hline CNN & 41.60 \(\pm\) 0.11\% & 46.05 \(\pm\) 0.13\% & 43.86 \(\pm\) 0.08\% & 61.79 \(\pm\) 0.07\% \\ TL & 44.19 \(\pm\) 0.43\% & 49.89 \(\pm\) 0.33\% & 40.26 \(\pm\) 0.60\% & 75.68 \(\pm\) 0.55\% \\ MAML & 55.32 \(\pm\) 0.23\% & 70.25 \(\pm\) 0.21\% & 65.19 \(\pm\) 0.18\% & 75.97 \(\pm\) 0.89\% \\ ProtoNet & 59.03 \(\pm\) 0.29\% & 72.44 \(\pm\) 0.30\% & 59.90 \(\pm\) 0.40\% & 72.41 \(\pm\) 0.38\% \\ TAPT & **64.16 \(\pm\) 0.25\%** & **77.61 \(\pm\) 0.63\%** & **70.86 \(\pm\) 0.43\%** & **81.12 \(\pm\) 0.25\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy comparison of 5-way ASC on the ANAFCC dataset (Bold and underlined values indicate the best and second-best accuracies, respectively). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Learning Scheme**} & \multicolumn{2}{c}{**4 conv**} & \multicolumn{2}{c}{**VGG11**} \\ \cline{2-5}  & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** \\ \hline CNN & 27.86 \(\pm\) 0.28\% & 36.24 \(\pm\) 0.12\% & 31.23 \(\pm\) 0.27\% & 40.48 \(\pm\) 0.11\% \\ TL & 32.39 \(\pm\) 1.95\% & 49.19 \(\pm\) 1.91\% & 45.68 \(\pm\) 1.37\% & 66.32 \(\pm\) 1.14\% \\ MAML & 45.83 \(\pm\) 1.01\% & 56.24 \(\pm\) 0.56\% & 59.23 \(\pm\) 0.93\% & 70.08 \(\pm\) 0.87\% \\ ProtoNet & 57.45 \(\pm\) 1.02\% & 76.08 \(\pm\) 0.67\% & 58.63 \(\pm\) 1.18\% & 70.15 \(\pm\) 1.15\% \\ TAPT & **64.45 \(\pm\) 0.49\%** & **83.48 \(\pm\) 0.43\%** & **66.85 \(\pm\) 0.79\%** & **84.41 \(\pm\) 0.42\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy comparison of 5-way ASC on the BirdVox-14 SD dataset (Bold and underlined values indicate the best and second-best accuracies, respectively). Furthermore, in the tables, few-shot learning schemes such as MAML and TL showed better ASC performance than CNN because they were trained with \(S_{\textit{task}}\) in addition to \(T_{\textit{target}}\). Among the few-shot learning schemes, MAML outperformed TL. One advantage of MAML over TL in few-shot ASC is generalization. As TL lacks the generalization process of knowledge during pretraining of the source task, its adaptability to new tasks in a few-shot setting is limited. In contrast, MAML updates the parameters of DeepASC in a way that generalizes the knowledge of the source task, resulting in better ASC accuracy in all cases. Despite MAML's superior performance, ProtoNet showed better classification performance than MAML in most cases. We found several advantages of ProtoNet compared to MAML in the context of few-shot ASC. First, ProtoNet computes the prototype of each class in a feature space, allowing for a more efficient representation of each class and making it easier to classify new classes based on their class prototypes. MAML, while powerful, learns an initialization that can quickly adapt to new tasks.