## 4 Training and Performance Evaluation of the CCBA Model
### Performance Evaluation and Results
#### 4.3.1 Classification Accuracy
 } & \multirow{3}{*}{**CNN-Audio block**} & \multirow{3}{*}{CNN-LSTM} & 1 & 28.3\% & 45.7\% & 65.4\% & 76.7\% & 82.5\% & 85.0\% & 86.0\% & 67.1\% \\ \cline{3-10}  & & & 2 & 28.4\% & 45.9\% & 66.4\% & 78.3\% & 84.5\% & 86.6\% & 87.8\% & 68.3\% \\ \cline{3-10}  & & & 1 & 27.1\% & 43.3\% & 65.2\% & 75.7\% & 81.6\% & 84.4\% & 86.8\% & 66.3\% \\ \cline{3-10}  & & & 2 & 28.2\% & 44.6\% & 65.9\% & 78.7\% & 84.6\% & 87.3\% & 88.1\% & 68.2\% \\ \cline{3-10}  & & & Dual-stream & 1 & 28.6\% & 46.5\% & 67.8\% & 78.3\% & 83.4\% & 86.5\% & 87.4\% & 68.3\% \\ \cline{3-10}  & & & ResNet18-CNN-LSTM & & 2 & 28.7\% & 45.9\% & 67.8\% & 78.4\% & 84.7\% & 87.1\% & 87.9\% & 68.6\% \\ \cline{3-10}  & & & **CNN-BiLSTM-Attention** & 1 & 30.3\% & 48.2\% & 69.8\% & 81.2\% & 85.3\% & 87.9\% & 89.0\% & 70.2\% \\ \hline \multirow{3}{*}{(Teacher)} & \multirow{3}{*}{MFCC} & \multirow{3}{*}{CNN-LSTM} & \multirow{3}{*}{-} & \multirow{3}{*}{17.4\%} & \multirow{3}{*}{25.3\%} & \multirow{3}{*}{40.3\%} & \multirow{3}{*}{50.8\%} & \multirow{3}{*}{60.1\%} & \multirow{3}{*}{66.3\%} & \multirow{3}{*}{94.9\%} & \multirow{3}{*}{50.7\%} \\  & & & & & & & & & & \\ \cline{3-10}  & & STFT (n = 512)-MFCC & \multirow{3}{*}{Multi-stream CNN CNN-BiLSTM} & \multirow{3}{*}{-} & \multirow{3}{*}{28.5\%} & \multirow{3}{*}{43.9\%} & \multirow{3}{*}{63.0\%} & \multirow{3}{*}{76.3\%} & \multirow{3}{*}{84.4\%} & \multirow{3}{*}{88.9\%} & \multirow{3}{*}{94.4\%} & \multirow{3}{*}{68.5\%} \\ \cline{3-10}  & & STFT (n = 1024)-MFCC & \multirow{3}{*}{Multi-stream CNN-BiLSTM} & \multirow{3}{*}{-} & \multirow{3}{*}{28.5\%} & \multirow{3}{*}{43.9\%} & \multirow{3}{*}{63.0\%} & \multirow{3}{*}{76.3\%} & \multirow{3}{*}{84.4\%} & \multirow{3}{*}{88.9\%} & \multirow{3}{*}{94.4\%} & \multirow{3}{*}{68.5\%} \\ \cline{3-10}  & & STFT (n = 2048)-MFCC & \multirow{3}{*}{Multi-stream CNN-BiLSTM} & \multirow{3}{*}{-} & \multirow{3}{*}{28.5\%} & \multirow{3}{*}{43.9\%} & \multirow{3}{*}{63.0\%} & \multirow{3}{*}{76.3\%} & \multirow{3}{*}{84.4\%} & \multirow{3}{*}{88.9\%} & \multirow{3}{*}{94.4\%} & \multirow{3}{*}{68.5\%} \\ \cline{3-10}  [38] & & Stockwell-MFCC & \multirow{3}{*}{Multi-stream CNN-BiLSTM} & \multirow{3}{*}{-} & \multirow{3}{*}{28.5\%} & \multirow{3}{*}{43.9\%} & \multirow{3}{*}{63.0\%} & \multirow{3}{*}{76.3\%} & \multirow{3}{*}{84.4\%} & \multirow{3}{*}{88.9\%} & \multirow{3}{*}{94.4\%} & \multirow{3}{*}{68.5\%} \\ \cline{3-10}  [38] & & WVD-MFCC & \multirow{3}{*}{(Post-mix) CNN-BiLSTM} & \multirow{3}{*}{-} & \multirow{3}{*}{28.5\%} & \multirow{3}{*}{43.  
accuracy. CCBA's average accuracy dropped by only 1.7% compared to the teacher model, significantly smaller than the 5.5% decrease in other classifiers. Additionally, the variance in accuracy decrease across SNR levels for CCBA is 0.22, considerably lower than the benchmark models, which show variances of 2.06, 2.46, and 1.76. This underscores CCBA's stable performance across varying SNR conditions. Figure 9 presents the confusion matrices for the teacher system and the CCBA, averaged across SNR levels. Labels 0 to 9 correspond to air conditioners, car horns, children playing, dog bark, drilling, engine idling, gunshots, jackhammer, sirens, and street music. CCBA shows accuracy decreases in 6 out of 10 classes, with the most significant drop of 4% for Car horns. However, in 3 classes, CCBA slightly surpasses the teacher system, with gains of up to 2%. Notably, many sounds across different categories are often misclassified as Drilling, both in the teacher system and CCBA. This suggests that CCBA has successfully learned and mirrored the teacher system's behavior in handling challenging sound categories, further validating its ability to emulate key classification patterns.