## 3 Experimental Results
The poor performance of other methods on minority classes is likely class imbalance in the dataset. Traditional models such as random forest (RF), k-nearest neighbors (KNNs), and One-vs.-All tend to favor majority classes, as they rely on handcrafted features that might not sufficiently capture the subtle differences between cry categories. When the dataset is imbalanced, these models often learn to optimize for the dominant class, leading to a significant drop in performance for underrepresented classes. Furthermore, traditional machine learning methods struggle with the complex and high-dimensional nature of baby cry sounds, which exhibit intricate variations in frequency, amplitude, and temporal patterns. These models typically depend on predefined statistical features, such as MFCCs or spectral features, which may not be expressive enough to distinguish minority classes effectively. On the other hand, deep learning models, particularly artificial neural networks (ANNs), have the advantage of learning hierarchical and abstract feature representations directly from the data. By leveraging advanced optimization techniques and neural architectures, these models are able to capture nuanced differences between cry types, even in cases where certain classes have fewer training samples. Additionally, our proposed method incorporates grid search (GS) for hyperparameter tuning, data augmentation (DA) to mitigate class imbalance, and MFCC-based feature extraction, all of which contribute to its superior performance. The ANN's ability to generalize well across different cry types, despite data imbalance, further highlights its effectiveness in this classification task. Future improvements could involve employing more sophisticated augmentation techniques or adaptive loss functions to further enhance performance across all classes. When the results are examined, the highest accuracy (90%) and F1 score (90%) values were achieved with the structure-tuned artificial neural network (ANN)-based proposed method. The proposed method showed the best performance in terms of accuracy and F1 score, exhibiting a higher classification success than the other methods. Therefore, the proposed method model was preferred as the most appropriate solution for classifying baby sounds. The 90% accuracy rate obtained was compared with the study that gave the best-known result in the literature. According to the results in Table 7, the proposed method left behind the most successful result in the same data splitting type. The similarities and differences between the proposed method for classifying baby sounds and the existing studies using the same feature extraction process are shown in Table 8.  
In this study, the performance of the proposed method was evaluated in detail using a confusion matrix, learning curves, and ROC curves. The confusion matrix for the test data results is given in Figure 5. The graph measuring the best performance of the learning model over the epoch or iteration is shown in Figures 6 and 7. The confusion matrix was used to evaluate the class-based accuracy and error rates of the model based on the test data. The majority of classes were correctly classified. The correct classification rates were high, especially for belly pain, burping, and discomfort (31, 16, and 49 correct classifications). Significant confusion was observed between hungry and tired. Hungry was frequently misclassified as tired (5 examples), and similarly, tired was \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Techniques** & **F1 Score** & **Accuracy** \\ \hline [PERSON] et al. [24] & ANN & 46.99\% & 85.93\% \\ Ours & **ANN + GS + DA + MFCC** & 90\% & 90\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison studies on Donate a Cry and accuracy rates. predicted as hungry in 7 examples. This situation shows that the features of these classes can be quite similar.