## 1 Introduction  
Babies cry as an innate reflex to express their needs, such as hunger, discomfort, and sleeplessness. Understanding these cries accurately and quickly is critical to babies' healthy development and happiness [1]. However, inexperienced parents, new caregivers, and some healthcare professionals may have difficulty understanding why babies cry. This can lead to prolonged crying, increased stress levels, and negatively impacted cognitive development [2]. Studies on the automatic classification of baby crying sounds have significantly contributed to this field. Analysis and classification of baby crying sounds using sound processing methods and machine learning algorithms constitute the basis of research in this field. This literature review examines important studies on the classification and detection of baby crying sounds. It discusses, in detail, the methodologies, datasets, feature extraction methods, and classification algorithms used in these studies. Various studies on the classification of baby cries have developed different methods using sound processing methods and machine learning algorithms. [PERSON] (2015) classified the conditions such as hunger, need to burb, abdominal pain, discomfort, fatigue, loneliness, feeling cold or hot, and fear on the Donate a Cry dataset and achieved 81.27% accuracy with k-means, hierarchical clustering, and Gaussian mixture models using features such as mean frequency, median frequency, spectral entropy, and skewness. [PERSON] (2016) achieved 94.97% success in classifying hunger, discomfort, need to pass gas, and abdominal pain using the Dunstan Baby dataset with CNN-RNN. Similarly, [PERSON] (2016) achieved 89% accuracy with CNN on the same dataset. [PERSON] (2016) achieved 76.4% success with the nearest neighbor algorithm and artificial neural networks using LPC, LPCC, MFCC, and BFCC features on sounds recorded in the NICU environment. [PERSON] (2016) achieved 86.1% accuracy with capsule networks' help on the CRIED dataset. [PERSON] (2016) performed classification using SVM, Bagging Decision Tree, and Boosted Trees algorithms using parameters such as spectrum, tone, zero crossing rate, root mean square, energy, and statistical features in the Dunstan Baby dataset. [PERSON] (2016) evaluated spectrogram-based features using CNN to classify hunger, pain, and sleep categories on the dataset collected from National Taiwan University Hospital with an accuracy rate of 78.5%. [PERSON] (2016) achieved 86% accuracy with KNN using features such as MFCC and short-term energy on its own recorded data. [PERSON] (2016) used features such as median, mean, standard deviation, and minimum and maximum values of F0 and F123 parameters to distinguish full-term and preterm babies and achieved 87% success with logistic regression, multilayer perceptron, SVM, and RF algorithms. [PERSON] (2016) achieved 91.58% accuracy with K-means clustering and KNN algorithms using MFCC and LFCC features on his own recorded data. [PERSON] (2016) achieved 97.96% accuracy by classifying hunger and pain states on the Baby Chillanto dataset using MFCC and LPC features using a fuzzy model algorithm. [PERSON] (2016) achieved 62.1% accuracy with the nearest neighbor algorithm using FFT features on his own recorded discomfort, hunger, and sleep categories. Finally, [PERSON] and colleagues (2016) achieved 99.9% accuracy using MFCC and artificial neural network (ANN) algorithms with the Baby Chillanto dataset. Various studies on baby crying detection have developed different approaches to overcome the challenges in this area using sound processing methods and machine learning algorithms. For example, [PERSON] (2016) performed baby crying detection using a self-recorded dataset. The data included background sounds such as television and speech, and 99.83% accuracy was achieved using the CNN algorithm with spectrogram-based features.