## 1 Introduction
[PERSON] (2016) extracted MFCC features by recording baby crying data accompanied by the air conditioner and fan sounds in a home environment and achieved an 86% accuracy rate using 1D-CNN, FFNN, and SVM algorithms. [PERSON] (2016) achieved 90% accuracy with the KNN algorithm using LFCC features on a dataset containing baby crying and non-crying sounds that she recorded. [PERSON] (2016) used LPC features on a dataset containing background sounds such as laughter and dog barking that she recorded and achieved a 97.1% success rate with the Dynamic time warping algorithm. [PERSON] (2016) worked on two different datasets: the first is real data recorded in a hospital's Neonatal Intensive Care Unit (NICU); the second is a dataset containing synthetic sounds such as speech and \"beep\" sounds. The CNN algorithm with Log-Mel coefficients features was used, and an accuracy rate of 86.58% was achieved in the real dataset and 92.92% in the synthetic dataset. [PERSON] (2016) worked on the TUT Rare Sound Events 2017 dataset. This dataset includes sounds such as glass breaking, gunshots, and baby cries. Using log-amplitude mel-spectrogram features, CRNNachieved an 85% success rate for baby cry detection and 87% for all targets. [PERSON] [22] worked on a dataset containing background sounds, such as adult crying and vacuum cleaners, collected from online sources. Features such as MFCC, voiced-unvoiced counter, and harmonic ratio accumulation with consecutive F0 were extracted from the audio data. A 92% AUC rate was achieved with the Support Vector Data Descriptor (SVDD) and CNN algorithms. [PERSON] [23] worked on a dataset containing background sounds such as talking and door opening in a home environment. Using the CNN algorithm with features such as MFCC, pitch, and formant, 95% accuracy was achieved. This study aims to develop a system that can automatically classify the needs or distresses underlying the crying sounds of babies by analyzing them. The system will extract meaningful data using sound processing techniques and machine learning algorithms and classify this data, allowing parents and caregivers to respond to babies' needs accurately and quickly. Sound processing methods such as Mel-Frequency Cepstral Coefficients (MFCCs) will be used in the research. The development of this system will support healthy development by reducing babies' stress levels. In addition, it will also contribute to the establishment of a healthier and happier relationship by reducing the stress of parents and caregivers. In addition, this study will contribute to the academic literature on sound processing and machine learning and will guide parents as a practical training and support tool. The main contributions of the article are as follows:  
* The study provides a more effective solution than existing approaches in this field by developing an ANN-based integrated model for classifying baby sounds. * The artificial neural network model used in the study was improved by hyperparameter optimization using the grid search optimization algorithm, and its accuracy rate was significantly increased. * To the best of our knowledge, the proposed model, which has the best average accuracy rate of 90%, outperforms the existing work [24] that reported an 85.93% accuracy on Donate a Cry Dataset test data. The rest of this paper is organized as follows. Materials and Methods are presented in Section 2. Section 3 describes the experimental studies, including the experimental setup and results. Finally, Section 4 summarizes the conclusion and future work.