## 5 Discussion
(2023), have further expanded the potential of CNNs in sound analysis by integrating fusion-based approaches and augmentation techniques. These  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Metric**} & **Air Condition-** & **Car** & **Children** & **Dog** & **Drilling** & **Engine** & **Gunshot** & **Jackhammer** & **Siren** & **Street** \\  & & **tioner** & **from** & **Playing** & **Bark** & **Drilling** & **Idling** & **Gunshot** & **Jackhammer** & **Siren** & **Music** \\ \hline \multirow{4}{*}{CNN} & Accuracy & 0.93 & 0.95 & 0.81 & 0.84 & 0.86 & 0.93 & 0.97 & 0.95 & 0.97 & 0.84 \\  & Precision & 0.93 & 0.95 & 0.84 & 0.82 & 0.92  
advancements highlight the growing applicability of CNNs in sound analysis, aligning with and supporting the findings obtained in this study, thereby validating the robustness of the proposed methodology [45; 46]. In addition to these general findings, CNN models have been specifically employed for urban sound classification using the UrbanSound8K dataset. In one study, data augmentation techniques, including pitch shifting, time stretching, and SpecAugment, were applied on a five-layer CNN model. This study, conducted in 2019, achieved its highest accuracy of 79.2% and an F1-score of 80.3% when SpecAugment and time-stretching techniques were combined [47]. In another study from 2021, a deep one-dimensional CNN model was proposed, where MFCCs were used for feature extraction. The model achieved an accuracy of 84.02%, indicating its capability to effectively classify diverse urban sounds [48]. More recently, in 2022, a different approach was taken by directly learning features from raw waveforms using a deeper CNN architecture. This method achieved a notable accuracy of 89%, demonstrating the potential of end-to-end learning approaches in urban sound classification [49]. Compared to these studies, the CNN model in the present work demonstrated superior performance with an accuracy of 90% and balanced macro and weighted F1-scores of 90% without employing advanced augmentation techniques. This result suggests that the architecture and preprocessing approach proposed here, particularly the use of MFCC-based feature extraction and the fog computing framework, contribute significantly to improving urban sound classification. While previous studies have shown that augmentation techniques can enhance model robustness, this study indicates that a well-structured CNN model with efficient preprocessing can achieve higher performance without extensive augmentation. Nonetheless, integrating augmentation methods could further improve the generalization and robustness of the model in real-world scenarios where varying acoustic conditions are encountered [50]. The LSTM model, while achieving a respectable overall accuracy of 81%, demonstrated lower performance in specific sound categories, such as \"children playing\" and \"dog bark\". In our study, the F1-scores for these categories were 70% and 68%, respectively, which are significantly lower than other categories such as \"engine idling\" (90%) and \"jackhammer\" (88%). This outcome aligns with findings in the literature, which indicate that LSTM models may face challenges in recognizing sounds that lack clear temporal structure or overlap with other categories. A similar study using the UrbanSound8K dataset reported an accuracy of 79% for LSTM models, further supporting the observation that LSTM models, while advantageous for sequence-based data, may struggle with less distinct or overlapping temporal patterns [51]. Additionally, it was noted in a 2023 study that while LSTM models are effective in handling sequential data, they were outperformed by Dense architectures in tasks involving non-sequential or complex overlapping sounds, suggesting that LSTM models may require additional fine-tuning or hybrid approaches to improve their performance in urban sound classification [52].