## 4 Experimental Results
### Dense Model Performance Results  
The analysis process began with the extraction of MFCC features, using the n_mfcc = 60 parameter. This value was chosen based on its ability to balance computational efficiency and feature richness, ensuring that sound signals' spectral characteristics were adequately represented. Three-second segments were extracted from the audio files with a 0.5 s offset. These values were determined through preliminary experimentation, ensuring appropriate temporal resolution for capturing key sound patterns. The resulting MFCC matrices were converted into a format suitable for the Dense model using the pad_sequences_2d function and standardized to a fixed length. The padding strategy was  
Figure 5: Confusion matrix for LSTM layers. set to \"post\" to preserve critical information at the beginning of the feature sequences. The MFCC feature extraction time for the Dense model was measured as 66.12 s.  
The dataset was divided into training and test sets using an 80-20% split, which was commonly adopted in the literature. Experiments with different ratios (70-30%, 75-25%, 85-15%) showed that the 80-20% split provided the best balance between model learning capacity and evaluation reliability. A 5-fold cross-validation approach was adopted, and the StratifiedKFold method was used with random_state = 0 to ensure reproducibility and balanced class distributions across folds. Labels were converted to numerical values using the LabelEncoder method and one-hot encoded for training. Stratified splitting was applied within each fold to maintain class balance. The Dense model consisted of three layers, with 125 neurons in the first layer, 250 neurons in the second layer, and 125 neurons in the third layer. This architecture was selected to enhance the model's capacity for learning complex sound patterns while maintaining reasonable training times. A dropout rate of 50% was applied after each layer to mitigate overfitting. This relatively high rate was selected based on initial tests, where lower dropout rates (20-30%) were found insufficient to prevent overfitting. The ReLU activation function was used in the hidden layers, and the softmax activation function was applied in the output layer for multi-class classification. This architecture and dropout strategy were determined through experimentation, ensuring optimal model performance. The ReLU activation function was chosen due to its ability to mitigate the vanishing gradient problem and improve convergence speed. The model was trained using the Adam optimizer, known for its adaptive learning rate and efficient convergence properties. The batch size was set to 32 to balance computational efficiency and generalization performance. The number of epochs was set to 100, based on observations of the training and validation curves, ensuring sufficient learning without excessive computational cost. Early stopping was not employed in this study. Instead, the number of epochs was determined based on the convergence analysis of the training and validation loss curves, ensuring that the model could fully learn the patterns without overfitting. The training accuracy of the Dense model initially started at a low value of 12.5% but progressively increased to 80.6% as training advanced. This initial low accuracy can be attributed to the complexity of the dataset and the presence of multiple distinct sound categories, which challenged the model's ability to learn accurate representations early in the training process. However, as the model continued to train, it gradually improved its accuracy by learning the intricate patterns within the dataset. Validation accuracy similarly exhibited an initial low point of 12.2%, eventually increasing to 85.6%, reflecting improved generalization capabilities over time. Training loss decreased from 10.0883 to 0.5895, while validation loss fell from 2.2881 to 0.5091, indicating that the model successfully learned to make more accurate predictions while avoiding overfitting due to the regularization techniques applied.