## 5 Experiments and Results
### Experimental Setup  
To ensure the consistency and comparability of model inputs, all audio samples were uniformly resampled to 16 kHz and segmented into 5 s clips. This standardization allows neural networks to receive fixed-length inputs, thereby reducing training bias caused by varying input durations. For feature extraction, 128-dimensional log-Mel spectrograms were adopted as the primary input representation. As a perceptually optimized temporal-frequency encoding, the log-Mel spectrogram strikes a balance between temporal and spectral resolution, closely aligns with human auditory sensitivity across frequency bands, and facilitates the extraction of both local and global audio patterns by deep neural networks.  
For model training, we employed the Adam optimizer, which is well-suited for handling non-stationary gradients and adaptive learning rates--particularly important in the presence of acoustically complex and variable urban noise conditions. The initial learning rate was set to 0.001, and the batch size was fixed at 24 to balance training stability with computational efficiency. Training was performed for 50 epochs using a learning rate schedule that combines linear warm-up in the initial phase with cosine annealing decay thereafter. This strategy helps the model converge smoothly during early training and mitigates overfitting in the later stages by gradually decreasing the learning rate.  
To further ensure evaluation fairness and consistency across datasets, we adopted a unified 75%/25% training-testing split for all three datasets as follows: ESC-50, Urban-Sound8K, and the proposed UN15. Although ESC-50 and UrbanSound8K each provide official partitioning protocols, their schemes are not aligned, making direct comparison infeasible. By re-dividing all datasets using a common standard, we ensure a reproducible and fair benchmarking setup.  
To enhance the model's robustness against missing information and local perturbations, we employed SpecAugment during the training phase. This data augmentation technique applies random masking along the time and frequency axes of the log-Mel spectrograms. Specifically, we implemented a custom augmentation module that, for each training batch, randomly masks frequency bands with widths in the range of [0, 8] and time intervals with widths in [0, 10]. This process simulates real-world challenges such as frequency band interference, event omissions, or device-related occlusions. Since this strategy operates at the feature level, rather than directly altering the raw audio waveform, it preserves the overall structure and semantic integrity of the audio while improving the model's generalization under uncertain or partially missing conditions.  
All experiments were conducted on a workstation equipped with an Intel Core i7-11700F CPU, 32 GB RAM (Intel, Santa Clara, CA, USA), and an NVIDIA GeForce RTX 4060 Ti GPU (16 GB) (Nvidia, Santa Clara, CA, USA). The deep learning framework was PyTorch 2.0.1, with CUDA 11.8 and cuDNN 8.9.1 enabled for acceleration. Audio features were extracted using Librosa 0.10.0 and Torchaudio 2.1.0, and all code was developed and executed in Python 3.10 on Windows 11.