## 4 Experimental Results
### Dense Model Performance Results
Table 3 presents a detailed performance evaluation of the Dense model on the Urban-Sound8K dataset. Overall accuracy on the test set was calculated at 84%, with a macro average accuracy of 86% and an F1-score of 84%. These metrics were chosen to evaluate the model's balanced performance across all classes, considering the potential class imbalances within the dataset. The macro average metric provides an equal-weighted performance evaluation across classes, while the weighted average accounts for class imbalances, thus offering a more realistic assessment of the model's general performance. According to the results, the model exhibited high accuracy and F1-scores in the \"car horn\" (91% F1-score) and \"siren\" (93% F1-score) categories. These high scores suggest that the model was proficient in recognizing these sounds, likely due to their distinct acoustic features. In particular, the \"car horn\" category achieved a precision of 97%, and the \"siren\"category achieved a recall of 90%, underscoring the model's ability to differentiate these categories effectively. The unique and high-frequency features of these sounds may have facilitated the model's ability to learn and classify them accurately. In contrast, the model's performance was lower in the \"children playing\" (68% F1-score) and \"gunshot\" (76% F1-score) categories. The \"gunshot\" category, in particular, exhibited a low recall of 63%, suggesting that the model had difficulty distinguishing this sound from other categories. This low recall may be due to the overlap in acoustic features between gunshot sounds and other sound types within the dataset. The relatively lower performance in the \"children playing\" category may be attributed to overlapping frequencies with other sounds, making it challenging for the model to differentiate this category. Similar difficulties in classifying these types of sounds have been reported in the literature, suggesting that these challenges are inherent to the dataset and the acoustic similarity of certain sounds. Overall, the Dense model's results are comparable to those reported in similar studies on urban sound classification, indicating a competitive accuracy level within the range of 82-85% commonly observed for Dense models on the UrbanSound8K dataset. Thus, the model's performance aligns with expectations based on the existing literature, further validating the effectiveness of the chosen architecture and preprocessing steps. While the model achieved high performance in certain categories, it showed lower accuracy and F1-scores in some classes, which can likely be attributed to the presence of overlapping acoustic features between sound categories. These findings demonstrate the Dense model's potential to provide balanced classification performance across diverse sound categories within a fog computing architecture for urban sound recognition applications. However, further testing with real-time data could offer additional insights into the practical applicability of this system in real-world settings. Figure 6 displays the confusion matrix for the Dense layers' performance on the UrbanSound8K dataset. The model generally classified sounds such as air conditioner, car horn, engine idling, and jackhammer correctly. However, noticeable confusion was observed in the categories of children playing, dog bark, and street music. For example, the children playing category was often confused with air conditioner and dog bark. Categories such as drilling and gunshot were mostly classified correctly, although there were occasional confusions with other categories. Critically evaluating the confusion matrix, the model shows strength in identifying distinct sounds like car horn and siren, likely due to their unique acoustic features. However,