## 5 Experiments and Results
### Result on UN15 Dataset  
We conducted extensive evaluations of various deep learning models on the UN15 dataset to assess their performance in environmental sound classification. The results are summarized in Table 4. Among the baseline models, CNN10 and CAM++ achieved classification accuracy of 82.45% and 82.22%, respectively, substantially outperforming traditional architectures such as TDNN and the shallower CNN6. These results suggest that deeper network architectures possess stronger representational capacity for modeling complex time-frequency patterns in environmental audio.  
Furthermore, incorporating attention mechanisms into the ResNet backbone significantly improved performance. Specifically, the ResNet-ECA and ResNet-SE models achieved accuracy of 83.21% and 84.44%, respectively, demonstrating the effectiveness of lightweight channel attention in enhancing model discriminability.  
Building on this, the proposed ResNet-TF model integrates a TF attention module, which enables simultaneous modeling of temporal and spectral dependencies. This model  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** &
\begin{tabular}{c} **Precision** \\ **Score** \\ \end{tabular} & **Recall** & **F1-Score** \\ \hline TDNN & 72.96\% & 76\% & 76\% & 75\% \\ CAM++ & 82.22\% & 82\% & 81\% & 81\% \\ CNN6 & 78.52\% & 80\% & 79\% & 78\% \\ CNN10 & 82.45\% & 83\% & 82\% & 82\% \\ ResNet50 & 81.24\% & 81\% & 82\% & 81\% \\ ResNet-ECA & 83.21\% & 84\% & 84\% & 84\% \\ ResNet-SE & 84.44\% & 84\% & 84\% & 83\% \\ ResNet-TF & 85.19\% & 86\% & 85\% & 85\% \\ \hline \hline \end{tabular} All performance metrics reported in this table are based on results obtained through our experimental setup described in Section 5.  
\end{table}
Table 4: Classification result of UN15 dataset.  
Figure 6: Confusion matrix of the ResNet-TF model evaluated on the UrbanSound8K dataset.  
attained the highest accuracy of 85.19% on the UN15 dataset, representing a new state-of-the-art benchmark.  
To further assess the model's discriminative performance, we present the confusion matrix of ResNet-TF on the UN15 test set in Figure 7. Most sound categories are accurately recognized, with near-perfect accuracy observed for rhythmically structured or high-energy classes such as air raid siren, car horn, and car, indicating strong feature extraction and classification capabilities for these well-defined sound events. However, several confusions are observed between acoustically similar classes, particularly those with overlapping spectral characteristics. For instance, the classification accuracy for helicopters is only 58%, with significant misclassifications into train (12%), lawnmower (12%), airplane (8%), and car (8%). These categories all exhibit broadband, low-frequency mechanical energy, typically with quasi-stationary temporal structures, leading to mutual masking in real-world sound mixtures. Specifically, helicopter rotor blades generate low-frequency pulse trains with periodic amplitude modulation, which can resemble Train rumble or lawnmower motor sounds in both time and frequency domains. Similarly, crowd shouting is often misclassified as crowd noise, due to their shared background noise and overlapping vocal energy; the distinction between emotional vocal bursts and ambient group chatter becomes blurred when the signal-to-noise ratio is low or the foreground sound lacks prominence. These results reveal that while ResNet-TF is effective for many structured sound events, future work could enhance its fine-grained separability for spectrally overlapping urban noise sources by incorporating signal enhancement, source separation, or hierarchical classification strategies.