## 3 Experimental Results  
This paper used VGG16 (a pre-trained CNN model consisting of 16 layers, which is frequently used in image classification tasks and is an important milestone in the field of deep learning), KNN, CNN, random forest-based methods (M1, M2, M3, M4, M5, and M6), and the ANN-based proposed method to classify baby sounds. These methods were applied to analyze the sound data to classify baby sounds. These methods were applied to analyze audio data to classify baby sounds. It is aimed to use these methods by repeating studies in the literature with high accuracy rates. In the testing phase of the models, the Donate a Cry dataset was tested and compared to classify baby sounds. In the simulations,  
Figure 4: Structure-tuned ANN for the proposed method. the dataset was divided into three parts, 75% for training, 10% for validation, and 15% for testing, see the splitting in [24] for a comparison. In the proposed method, hyperparameter optimization was performed using the grid search (GS) algorithm to tune the parameters of the ANN. The near-optimal hyperparameters selected by GS are shown in Table 5.  
While using the grid search optimization algorithm, not only the hyperparameter optimization but also the optimization algorithm to be used is optimized. In this process, three different optimizer options are presented to the grid search algorithm: Adam (DeepMind, London, UK), RMSProp ([PERSON], Toronto, ON, Canada), and SGD (Stochastic Gradient Descent, USA). Thus, the performance of the model is increased by determining the most appropriate optimizer and hyperparameter combination. In Table 6, the results compare the performance of different models to classify baby voices. The models were supported by methods such as DA (Data Augmentation), MFCC (Mel-Frequency Cepstral Coefficients), and SF (Spectral Flatness) as sound features. Precision, recall, and F1 score values were examined as performance measures. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Techniques** & **Metric** & **Belly Pain** & **Burping** & **Discomfort** & **Hungry** & **Tired** \\ \hline \multirow{3}{*}{M1} & \multirow{3}{*}{RF + DA + MFCC} & Precision & 0.00\% & 0.00\% & 18.00\% & 83.00\% & 0.00\% \\  & & Recall & 0.00\% & 0.00\% & 4.00\% & 88.00\% & 0.00\% \\  & & F1 Score & 0.00\% & 0.00\% & 8.00\% & 85.00\% & 0.00\% \\ \hline \multirow{3}{*}{M2} & \multirow{3}{*}{One-vs-All + DA + SF} & Precision & 0.00\% & 0.00\% & 0.00\% & 83.00\% & 0.00\% \\  & & Recall & 0.00\% & 0.00\% & 0.00\% & 100.00\% & 0.00\% \\  & & F1 Score & 0.00\% & 0.00\% & 0.00\% & 91.00\% & 0.00\% \\ \hline \multirow{3}{*}{M3} & \multirow{3}{*}{KNN + DA + SF} & Precision & 0.00\% & 0.00\% & 50.00\% & 85.00\% & 0.00\% \\  & & Recall & 0.00\% & 0.00\% & 4.00\% & 99.00\% & 0.00\% \\  & & F1 Score & 0.00\% & 0.00\% & 8.00\% & 91.00\% & 0.00\% \\ \hline \multirow{3}{*}{M4} & \multirow{3}{*}{CNN + MFCC} & Precision & 0.00\% & 0.00\% & 0.00\% & 77.00\% & 0.00\% \\  & & Recall & 0.00\% & 0.00\% & 0.00\% & 100.00\% & 0.00\% \\  & & F1 Score & 0.00\% & 0.00\% & 0.00\% & 87.00\% & 0.00\% \\ \hline \multirow{3}{*}{M5} & \multirow{3}{*}{VGG16 + MFCC} & Precision & 0.00\% & 0.00\% & 0.00\% & 87.00\% & 0.00\% \\  & & Recall & 0.00\% & 0.00\% & 0.00\% & 97.00\% & 0.00\% \\  & & F1 Score & 0.00\% & 0.00\% & 0.00\% & 92.00\% & 0.00\% \\ \hline \multirow{3}{*}{M6} & \multirow{3}{*}{Alexnet + Split + MFCC} & Precision & 0.00\% & 0.00\% & 0.00\% & 87.00\% & 0.00\% \\  & & Recall & 0.00\% & 0.00\% & 0.00\% & 100.00\% & 0.00\% \\  & & F1 Score & 0.00\% & 0.00\% & 0.00\% & 93.00\% & 0.00\% \\ \hline \multirow{3}{*}{Proposed} & \multirow{3}{*}{ANN + GS + DA + MFCC} & Precision & 94.00\% & 100.00\% & 96.00\% & 90.00\% & 79.00\% \\  & & Recall & 97.00\% & 100.00\% & 91.00\% & 81.00\% & 92.00\% \\ \cline{1-1}  & & F1 Score & 95.00\% & 100.00\% & 93.00\% & 85.00\% & 85.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Hyperparameter values of the ANN selected by GS.