# A Bird Vocalization Classification Method Based on Bidirectional FBank with Enhanced Robustness
## 5 Experiment and Result Analysis
### Discussion  
To further verify the robustness of the iWAVE-BiFBank method, boxplots of the accuracy of the results for 10 experiments are shown in Figure 14. iWAVE-BiFBank has good robustness and stability, with high accuracy. Furthermore, the robustness of FBank and iFBank based on iWAVE is significantly higher than that of the traditional MFCC and FBank.  
The proposed method is compared with other state-of-the-art methods in Table 7. Previous studies usually focused on feature engineering without denoising processes, resulting in framework instability [46]. The introduction of iWAVE enhances the robustness of classification performance. Furthermore, as shown in Table 7, researchers have frequently combined MFCC and/or FBank with machine learning and deep learning techniques [25; 26; 28; 29; 47; 48; 49; 50], because MFCC and FBank generate filter banks based on the Mel scale, ignoring high-frequency information in the original audio and therefore affecting classification performance. iFBank extracted on the iMel scale focuses on high-frequency information, while BiFBank can fully integrate low- and high-frequency information. Furthermore, when processing the same frame, BiFBank operations can be carried out simultaneously in the same code loop, so the proposed method does not increase the amount of computational complexity significantly.  
In summary, the iWAVE-BiFBank method achieves higher-accuracy bird vocalization classification, and iWAVE effectively reduces the interference of noise in the classifier. The BiFBank feature effectively addresses the limitations of traditional methods by focusing on both the low-frequency and high-frequency characteristics of the audio. This method has good noise reduction performance, classification performance, and robustness. At the same time, the proposed method also has the potential to be combined with emerging deep learning models. BiFBank, as a handcrafted feature, mainly focuses on the essence characteristics in audio, while the deep learning models extract the high-level features in  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Year** & **Method** & **Number of Bird Species** & **Performance/\%** \\ \hline Ntalampiras [47] & 2018 & MFCC with hidden Markov model (HMM) -based RF & 10 & Accuracy: 92.5 \\ [PERSON] et al. [46] & 2021 & STFT with multi-layer perceptron (MLP) & 8 & Accuracy: 96.1, Precision: 84.5, Recall: 82.6 \\ [PERSON] et al. [48] & 2022 & MFCC with ensemble-optimized extreme learning machine (ELM) & 9 & Accuracy: 89.05 \\ [PERSON] et al. [49] & 2022 & FBank and MFCC with long short-term memory (LSTM) & 264 & Accuracy: 74.94 \\ [PERSON] et al. [25] & 2023 & Combined MFCC, GTCC, NB, KNN, NN, GMM, and PSO & 264 & Accuracy: 78.33 \\ [PERSON] et al. [26] & 2023 & FBank with transformer model & 10 & Accuracy: 85.77 \\ [PERSON] et al. [28] & 2023 & FBank with full CNN & 17 & Accuracy: 85.71 \\ [PERSON] et al. [29] & 2024 & 3D-FBank with Phylogenetic Perspective Neural Network (PPNN) & 500 & Accuracy: 89.95 \\ [PERSON] et al. [50] & 2024 & MFCC with CNN, LSTM, and VGGish & 5 & Accuracy: 87.37 \\ Proposed method & 2024 & iWAVE-BiFBank & 16 & Accuracy: 94.00, F1 \\  & & & & score: 93.57, Kappa: 93.03, Precision: 93.54, Recall: 93.48 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparisons with other methods.  
audio. Combining them can provide a multi-view analysis on prospective features that enhance classification accuracy and generalizability.