# A Bird Vocalization Classification Method Based on Bidirectional FBank with Enhanced Robustness
## 5 Experiment and Result Analysis
### Result Analysis  
Group I: comparison with other denoising methods  
(1) Verification of the adaptive determination of the optimal decomposition level \begin{table}
\begin{tabular}{l l} \hline \hline
**Object** & **Relevant Parameter Settings** \\ \hline Wavelet basis & sym3 [44] \\ MFCC, FBank, & randomSeed: 77, pre-emphasis parameter: 0.97, frame size: 512, \\ iFBank, & frameshift: 256, overlap rate: 0.5, window: Hamming window, \\ BiFBank & numFilters: 40, numMFCCcoefficients: 13 \\ RF [14] & numTrees: 100, maxDepth: 0, randomSeed: 0, numFeatures: \\  & \(1+\log_{2}N\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameter settings in experiments. Figure 9: From left to right are the signals in the original, 0.01, 0.5, 3, and 10 noise intensity. Figure 10 presents the comparison results. The red dotted line represents the limit of \(\text{Corr}_{i}^{2}\); when this value is exceeded, the loop terminates, and the optimal decomposition level is the current number of levels minus one. When the \(\text{Corr}_{i}^{1}\) value falls below 0.8, the loop immediately terminates, and the optimal decomposition level is set to the current level. To represent this point more specifically, autocorrelation coefficients were selected when the noise intensity was three, as shown in Figure 10. In addition, the SNR value increased up to the optimal decomposition level, and, beyond this point, the SNR decreased significantly. (2) Verification of the optimized threshold selection rule's effectiveness  
The proposed method was compared with other methods in different noise environments, and the results are shown in Table 4. Among them, the method with the highest SNR value is bolded (values within a 0.01 difference are also bolded). In most cases, the proposed method has the best performance. (3) Verification of the improved threshold function's effectiveness  
The simulation signals were used to calculate the SNR to objectively reflect the performance of the proposed method, and the real signals were chosen to subjectively observe waveform differences after denoising with different threshold functions. Table 5 presents the performance of the three threshold functions in different noise environments. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Threshold Selection Rule**} & \multicolumn{4}{c}{**Blocks**} & \multicolumn{4}{c}{**Bumps**} \\ \cline{2-9}  & **0.01** & **0.5** & **3** & **10** & **0.01** & **0.5** & **3** & **10** \\ \hline RigrSure [20] & **44.23** & 34.33 & 29.35 & 25.23 & 39.85 & 30.10 & **25.12** & 20.99 \\ [PERSON] [21] & 41.94 & 34.11 & 29.34 & 25.31 & **40.12** & **30.12** & 25.09 & **21.11** \\ [PERSON] [34] & 42.83 & 33.62 & 28.68 & 24.68 & 38.59 & 29.35 & 24.38 & 20.37 \\ [PERSON] et al. [38] & 43.97 & 34.00 & 29.15 & 25.32 & 40.09 & 30.09 & 25.09 & 21.09 \\ [PERSON] et al. [39] & 41.73 & 32.28 & 27.24 & 23.20 & 37.41 & 27.97 & 22.93 & 18.88 \\ [PERSON] et al. [40] & 38.81 & 29.32 & 24.22 & 20.12 & 34.48 & 24.99 & 19.89 & 15.78 \\ [PERSON] et al. [41] & 42.83 & 33.63 & 28.68 & 24.68 & 38.59 & 29.36 & 24.38 & 20.37 \\ Proposed Method & 44.02 & **34.35** & **29.40** & **25.42** & 39.86 & **30.11** & **25.12** & **21.12** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: SNR values (dB) of the proposed rule and other rules. Figure 10: Application of the adaptive determination of the optimal decomposition level. As shown in Table 5, in all cases, the hard threshold outperforms the soft threshold. However, the hard threshold causes signal discontinuity, resulting in a pseudo-Gibbs phenomenon, which is undesirable. Conversely, the soft threshold avoids these issues, but it introduces a permanent deviation in the signal, which is also undesirable. The proposed threshold function maintains optimal performance in most cases while maintaining signal continuity and avoiding signal deviation.