## 2 Materials and Methods
### Proposed Method  
The primary objective of this study is to develop an effective integrated classification system to distinguish different types of baby crying sounds. The proposed method consists of four main phases: data augmentation, feature extraction, hyperparameter tuning, and model training. The overall architecture of the proposed method is illustrated in Figure 3.  
Phase 1: Data Augmentation. In order to improve the generalization ability of the model and prevent overfitting, data augmentation techniques are applied to the raw audio signals. Augmentation is particularly important in deep learning applications where the amount of available training data is limited [37]. In this phase, data augmentation is applied to audio data using time shifting, time stretching, pitch shifting, adding white noise, and slice methods. Data augmentation was performed for the entire set. While splitting, care was taken to ensure that the real and augmented data were not present in both the train and test sets. More details about the data augmentation techniques are provided in Section 2.3.1.  
Phase 2: Feature Extraction Mel-Frequency Cepstral Coefficients (MFCCs) are widely used in speech and sound processing because they effectively capture the spectral characteristics of an audio signal in a way that is similar to human auditory perception. In this phase, MFCCs are extracted from the augmented audio samples. The extracted MFCC  
Figure 3: The architecture of the proposed method.  
features serve as input to the classification model. Further details on feature extraction can be found in Section 2.3.3.  
Phase 3: Hyperparameter Tuning To optimize the classification performance of the artificial neural network (ANN), hyperparameter tuning is performed using the grid search (GS) algorithm. Grid search systematically explores a predefined set of hyperparameter values to find the combination that yields the best performance. The parameters optimized in this study include the following:  
* Number of Hidden Layers and Neurons: The architecture of the ANN is fine-tuned to balance model complexity and computational efficiency.
* Learning Rate: The step size for weight updates is optimized to ensure efficient convergence.
* Batch Size: The number of training samples used in each iteration is adjusted to optimize training stability.
* Activation Functions: Various activation functions (e.g., ReLU, Sigmoid, Tanh) are evaluated for their impact on model performance.
* Optimizer Selection: In addition to hyperparameter tuning, the optimization algorithm itself is also optimized. Three different optimizers--Adam, RMSProp, and SGD--are evaluated to determine the most suitable choice for the model.  
The optimized structure of the ANN is illustrated in Figure 4.  
Phase 4: Model Training and Evaluation The final stage involves training and evaluating the ANN model using the Donate a Cry Dataset. The dataset is split into training and testing subsets, ensuring that the model is trained on a diverse set of crying sounds and evaluated on unseen samples.  
The training process includes the following:  
* **Weight Initialization:** The model weights are initialized using an appropriate strategy to prevent vanishing or exploding gradients.
* **Backpropagation and Optimization:** The loss function is minimized using the selected optimizer, and model weights are updated accordingly.
* **Performance Evaluation:** The trained model is tested on unseen crying sounds, and its classification accuracy, precision, recall, and F1-score are computed to assess its effectiveness.  
Through these systematic steps, the proposed method ensures robust classification of baby crying sounds by leveraging data augmentation, feature extraction, hyperparameter tuning, and optimized model training.