# Improved Broad Learning System for Birdsong Recognition
## 5 Experiment and Result Analysis
### Analysis of Experiment Results  
With the sliding window approach, we set the size of \(w\) to 4, and the \(p\) size to 1. By combining the three features (MFCC, \(\Delta\)MFCC, and \(\Delta\Delta\)MFCC), we obtain \(d_{MFCC}\) features (39 dimensions). Subsequently, four frames of audio data are combined into feature sequences of a sample (156 dimensions). The final generated training and test samples are  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Classifier** & **Relevant Parameter Settings** \\ \hline BLS & activation function: tanh, epoch:50, N1:10, N2:100, N3:1000, C: 2\({}^{-30}\); s: 0.8 \\ ELM & hidden\_layer\_size:1000, activation function: sigmoid \\ MLP & hidden\_layer\_size:100, activation function: ReLU \\  & alpha: 0.0001, solver: [PERSON], learning\_rate\_init: 0.001, max\_iter: 200 \\ RF & n\_estimators:100, random\_state: 0, criterion: gini, max\_depth: none \\ SVM & kernel: rbf, gamma: auto, catch\_size: 200, max\_iter: \(-1\), degree: 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Setting of experimental parameters.  
From Table 3 and Figure 5, it shows that for each model the \(d_{square}\) feature is superior to the other two sets of features in all four evaluation indicators listed. The constructed feature \(d_{square}\) has increased by 15% in all evaluation indicators. On the aspect of standard deviation, the \(d_{square}\) is the lowest than the other two features, which reflects that it has a more stable performance among all features. In conclusion, the \(d_{square}\) feature proves to be effective for birdsong recognition.  
It can be seen from Table 3 and Figure 6 that for the constructed \(d_{square}\) features, compared with BLS and Res-BLS, the accuracy rate of RMSC-BLS is 92.37%, which is 2.38% and 2.30% higher than that of BLS and Res-BLS, respectively. The Res-BLS has a slight improvement over the BLS. From the comparison of Table 3, Figure 5, and Figure 6, it is evident that RMSC-BLS outperforms all other models, exhibiting the highest accuracy with an improvement ranging from 2.38 to 17.60%. In addition, _F1_, _Pre_, and _Recall_ are 91.99%, 92.61% and 91.59%, respectively, which increased by 17.66-2.41%, 16.92-2.14%, 17.78-2.56% compared with other models, respectively.  
The comparison of the experimental results of different models is shown in Figure 7. Through comprehensive analysis and comparison of the three models of experiments, it illustrates that the proposed method has superior performance when compared with other models. The results on _Acc_, _F1_, _Pre_, and _Recall_ are superior to BLS and Res-BLS models. As a whole, the RMSC-BLS has better generalization performance for the birdsong identification.  
From Table 4 and Figure 8, it can be observed that for the \(d_{square}\) feature data, the performance of the Res-BLS and MSC-BLS models is similar in various indicators. However, combining Res-BLS and MSC-BLS to form the RMSC-BLS model, there is an improvement in each indicator. In the experimental schemes, the RMSC-BLS model achieves better classification results compared to other models, with a minimum and maximum improvement _Acc_ of 2.3% and 2.33%, respectively. Other indicators also show some improvement. Overall, the proposed RMSC-BLS model has better classification results than other models in experimental schemes and achieves superior recognition results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**Performance (\%)**} \\ \cline{2-5}  & _Acc (mean \(\pm\) std)_ & _F1 (mean \(\pm\) std)_ & _Pre (mean \(\pm\) std)_ & _Recall (mean \(\pm\) std)_ \\ \hline Res-BLS & 90.07 \(\pm\) 0.29 & 89.58 \(\pm\) 0.32 & 90.47 \(\pm\) 0.34 & 89.02 \(\pm\) 0.32 \\ MSC-BLS & 90.04 \(\pm\) 0.30 & 89.56 \(\pm\) 0.32 & 90.48 \(\pm\) 0.31 & 88.97 \(\pm\) 0.33 \\ RMSC-BLS & 92.37 \(\pm\) 0.25 & 91.99 \(\pm\) 0.27 & 92.61 \(\pm\) 0.29 & 91.59 \(\pm\) 0.27 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of three module experiments.  
Figure 7: Comparison of different BLS models.