## 4 Training and Performance Evaluation of the CCBA Model
### Performance Evaluation and Results
#### 4.3.2 Computational Efficiency on Jetson Nano  
Table 2 presents the computational complexity and time costs of classifying a 6.144-s audio segment (6 \(\times\) 8192 samples) on the Jetson Nano. The CCBA model, comprising 2.83 million parameters, requires only 0.65 GFLOPs, whereas the classifier in the teacher system (dual-stream ResNet50-CNN-LSTM) demands 1.29 GFLOPs and 24.5 million parameters. This substantial reduction in model size and computational complexity leads to faster processing times. Figure 9: Averaged confusion matrices along SNR levels: (**a**) Results achieved by the teacher system; (**b**) results achieved by the CCBA. Regarding timing performance, using the CNN-Audio block significantly reduces the CPU time required for feature extraction from 21.319 s in the teacher system to just 0.111 s, achieving a remarkable 192-fold speed-up. When leveraging GPU acceleration, the execution time of the CNN-Audio block is further reduced to 0.014 s, offering an 8-fold improvement over CPU execution. Additionally, the teacher system requires 0.387 s to classify features on the GPU, considerably longer than the classification times achieved by the proposed model in this study. Considering the overall classification process, fully GPU-based CCBA delivers up to a 71-fold speed improvement compared to the teacher system, highlighting its computational advantage. Based on these findings, the Jetson Nano, when using CCBA, theoretically supports real-time urban sound classification for up to 20 sensor nodes simultaneously (6.144 \(\div\) (0.014 + 0.29)), making it highly suitable for scalable deployment in urban environments. Moreover, we analyzed the computational complexity and time costs of two existing systems. Our previously proposed CNN-LSTM using MFCC features [21] has only 0.99 million parameters and 0.014 GFLOPs, exhibiting high computational speed. However, this system is impractical due to its low classification robustness in noisy environments. The reproduced eight-stream CNN system utilizes MFCC features extracted from multiple time-frequency distributions and is used to compare with the proposed CCBA. Computing these eight features on the Jetson Nano takes considerable time. As a result, we terminated the program when the runtime exceeded 300 s, concluding that this time-consuming method, due to the large number of features and the complexity of the calculations, is unsuitable for real-time edge computing applications. The eight-stream CNN model has 0.97 million parameters and 4.60 GFLOPs, requiring 0.46 s to complete the classification task using the fake features as inputs. Therefore, considering overall classification accuracy and computational speed, the CCBA outperforms the schemes proposed in these two studies. These results demonstrate the efficiency of the CCBA in terms of computational complexity and processing speed, particularly highlighting the significant improvement in feature computation speed due to the CNN-Audio block. Overall, the proposed CCBA model maintains high classification accuracy across various SNR levels and substantially improves computational efficiency. This makes it highly suitable for real-time applications on edge devices with limited computational resources, fulfilling the practical deployment requirements for urban sound classification tasks.