## 4 Experimental Results
### LSTM Model Performance Results  
The analysis process began with the extraction of MFCC features, using the n_mfcc = 40 parameter. This parameter was chosen to adequately represent the spectral characteristics of sound signals with sufficient resolution and is commonly preferred in the literature. Alternative n_mfcc values (e.g., 20, 30, and 50) were tested, and 40 was found to provide an optimal balance between feature richness and computational efficiency. Three-second segments were extracted from the audio files with a 0.5 s offset. These parameters were evaluated to provide an appropriate temporal resolution for capturing the characteristic features of emotional expressions. The resulting MFCC matrices were converted into a format suitable for the LSTM model and standardized using the pad_sequences_2d function. The padding strategy was set to \"post\", ensuring that critical information at the beginning of the feature sequences was preserved. The dataset was processed using a 5-fold cross-validation approach, employing the StratifiedKFold class to ensure balanced class distributions across folds. Labels were converted to numerical values using the LabelEncoder method and one-hot encoded for training. An 80-20% training-validation ratio was maintained within each fold to ensure a proportional representation of classes. Figure 4: Confusion matrix for CNN layers. The LSTM model consisted of three layers, with 128 neurons in the first layer, 256 neurons in the second layer, and 128 neurons in the third layer. A relatively high dropout rate of 50% was applied after each LSTM layer to prevent overfitting as initial experiments indicated that lower dropout rates (e.g., 20-30%) resulted in insufficient regularization. The return_sequences = True parameter was used in all LSTM layers to preserve temporal information for subsequent layers. The softmax activation function was used in the output layer for multi-class classification. During training, the Adam optimization algorithm was used as the optimizer due to its adaptive learning rate strategy, which allows for an efficient convergence and effective handling of gradient issues. The batch size was set to 32, which was observed to provide adequate generalization performance while efficiently utilizing modern GPUs' memory capacity. The number of epochs was set to 100. This number was determined based on observations of the convergence behavior of the training and validation curves. Using fewer epochs resulted in insufficient learning, while increasing the number of epochs beyond 100 significantly raised computational costs without notable improvement in performance. Early stopping was not employed in this study as it was considered beneficial for the model to undergo uninterrupted training for the specified number of epochs to fully learn the patterns in the dataset. This decision was based on the assumption that uninterrupted training would allow the model to fully learn patterns in the UrbanSound8K dataset without premature halting. Compared to similar studies on urban sound recognition using the UrbanSound8K dataset, where typical LSTM models achieve between 78% and 83% accuracy, the achieved 81% accuracy aligns closely with benchmark results in the literature, thereby validating the effectiveness of the model's architecture and training strategy [36]. As shown in Table 2, when examining individual categories, the \"engine idling\" category stood out with the highest accuracy (0.94) and F1-score (0.90), demonstrating the model's proficiency in distinguishing engine idling sounds from other noises. Other categories such as \"air conditioner\", \"jackhammer\", and \"siren\" also exhibited relatively high performance, attributed to their distinct acoustic features, which are easier for the model to differentiate. However, the model's performance in the \"children playing\" (F1-score 0.70) and \"dog bark\" (F1-score 0.68) categories was relatively lower.