# Improved Broad Learning System for Birdsong Recognition
## 5 Experiment and Result Analysis
### Discussion  
In this study, we demonstrate that the constructed \(d_{square}\) features exhibit superior results compared to the original MFCC feature parameters of birdsongs. The constructed \(d_{square}\) features enhance the expression ability of features, which is crucial for birdsong identification in complex environments. Many previous studies have also used MFCC features for birdsong recognition. For instance, [PERSON] improved ELM by using differential evolution to classify MFCC features of nine birdsongs, with a maximum accuracy of 89.05% [46]. [PERSON] et al. fused Mel-spectrogram and MFCC as input features and used LSTM to recognize 264 birdsongs, with an average accuracy of 77.43% [47]. [PERSON] et al. combined the improved GTCC feature with probability enhanced entropy to classify twenty bird sounds in Borneo using SVM, with an accuracy of 89.5% [48]. At the same  
Figure 8: Comparison with Res-BLS, MSC-BLS and RMSC-BLS.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Performance (\%)**} \\ \cline{2-5}  & _Acc_ & _F1_ & _Pre_ & _Recall_ \\ \hline RF & 91.33 & 91.13 & 92.75 & 90.06 \\ SVM & 85.47 & 84.63 & 86.31 & 83.75 \\ MLP & 90.84 & 90.25 & 90.88 & 90.19 \\ ELM & 86.92 & 86.18 & 87.57 & 85.52 \\ RMSC-BLS & 92.37 & 91.99 & 92.61 & 91.59 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of RMSC-BLS with other methods.  
time, we also use ResNet18 with MFCC spectral features, achieving an accuracy of 92.11%, though this took a long time.  
For the application of deep learning in the field of birdsong recognition, [PERSON] et al. used RNN-LSTM to explore audio feature extraction techniques (Mel Spectrograms and MFCC) for bird identification of 91 species, and ultimately achieved an accuracy of 44.26% on the validation set [49]. [PERSON] et al. used spiking neural network with permutation pair frequency matrix to classify 14 bird species, with an accuracy of 92% [50]. [PERSON] et al. used deep learning models to identify the Mel Spectrograms and MFCC of 91 bird species, achieving an accuracy of 44.26% [51]. [PERSON] et al. used deep learning to combine two acoustic and visual features for late fusion and classified 14 birdsongs. The final best classification F1 score was 95.95% [52]. [PERSON] et al. used CNN to identify the Wavelet transform (WT) [53], short-time Fourier transform (STFT) [54], and Hilbert-Huang transform (HHT) [55] spectral features of 16 bird species, achieving accuracy rates of 89.11%, 88.36%, and 81%, respectively [56]. On this basis, three spectral features were further applying CNN to obtained CNN-WT, CNN-STFT, and CNN-HHT features. The results obtained using our proposed model RMSC-BLS were 91.22%, 90.78%, and 85.20%, respectively.  
Although these studies have made significant improvements, the lack of standardized benchmark datasets makes it difficult to directly compare methods even using the same indicators. From the above research, it can be concluded that the proposed RMSC-BLS model achieves a higher classification accuracy than most of these birdsong classification methods. These results clearly indicate that the improved BLS is successful in birdsong classification, as it can more fully express the sample information of birdsong.