## 4 Conclusions  
In this paper, different artificial intelligence models were applied and tested to classify baby sounds. The performances of pre-trained VGG16, KNN, CNN, random forest, and artificial neural network (ANN) models were evaluated using the Donate a Cry dataset. The success rates of the models increased thanks to the hyperparameter optimizations made with the grid search algorithm. The results showed that the test accuracy reached 90% in the proposed method, especially obtained using a five-layer artificial neural network (ANN). This success rate reveals the effectiveness of deep learning methods in correctly classifying baby crying sounds.  
The important outcomes of the study include the effectiveness of artificial intelligence models, data processing techniques, and augmentation methods. The proposed method provided high accuracy in classifying baby sounds. In addition, features such as Mel-Frequency Cepstral Coefficients (MFCCs), time, and spectral domain features significantly  
Figure 7: ROC curve for ANN.  
increased the classification performance. Data augmentation techniques, such as time shifting, pitch shifting, and white noise augmentation methods, enriched the dataset and strengthened the model's generalization ability.  
The results of this study have potential applications in health services and infant care. In particular, they can contribute to the development of real-time systems that can analyze baby crying sounds to provide fast and accurate information to parents or healthcare professionals.  
Future work can focus on testing models using larger datasets and developing systems optimized for real-time applications. Furthermore, using different acoustic features and more advanced deep learning architectures can further improve classification performance.  
Conceptualization, T.O. and H.G.; methodology, T.O. and H.G.; data collection, H.G.; writing--original draft preparation, H.G.; writing--review and editing, T.O. and H.G.; visualization, H.G.; supervision, T.O.; project administration, T.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ecriyes University Scientific Research Projects Unit with project number FYL-2024-14425.  
Not applicable.  
Publicly available dataset was analyzed in this study. This data can be found here: [[https://github.com/gveres/donatearcy-corpus](https://github.com/gveres/donatearcy-corpus)]([https://github.com/gveres/donatearcy-corpus](https://github.com/gveres/donatearcy-corpus)) (accessed on 12 September 2024).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
ANN Artificial Neural Network  
AUC Area Under Curve  
BFCCs Bark-Frequency Cepstral Coefficients  
CNN Convolutional Neural Network  
CRIED Cry Recognition and Infant Emotion Detection dataset  
CRNN Convolutional Recurrent Neural Network  
cu12 CUDA Toolkit Version 11.2  
DA Data Augmentation DCT Discrete Cosine Transform  
F0 Fundamental Frequency  
F123 First, Second, and Third Formant Frequencies  
FFT Fast Fourier Transform  
GS Grid Search  
KNN k-Nearest Neighbors  
LFCC Linear Frequency Cepstral Coefficient  
LPC Linear Predictive Coding  
LPCC Linear Predictive Cepstral Coefficient  
LogSoftmax Logarithmic Softmax Activation  
M1 Random Forest, Data Augmentation, Mel-Frequency Cepstral Coefficients  
M2 One-versus-All, Data Augmentation, Spectral Flatness  
M3 k-Nearest Neighbors, Data Augmentation, Spectral Flatness  
M4 Convolutional Neural Network, Mel-Frequency Cepstral Coefficients  
M5 Visual Geometry Group 16-layer network, Mel-Frequency Cepstral Coefficients  
M6 Alexnet, Data Split, Mel-Frequency Cepstral Coefficient  
MFCC Mel-Frequency Cepstral Coefficient  
NICU Neonatal Intensive Care Unit