# Evaluating Convolutional Neural Networks and Vision Transformers for Baby Cry Sound Analysis
## 1 Introduction  
The Internet of Things (IoT) has revolutionized various aspects of our daily lives, and its influence extends to the care and development of children and infants [1, 2]. IoT-enabled devices bring about significant improvements in health monitoring, safety, and education for young ones [3, 4, 5]. For example, smart baby monitors equipped with sensors can monitor an infant's vital signs, sleep patterns, and room conditions, providing real-time alerts to parents and caregivers [6]. These devices not only ensure a higher level of safety but also enable prompt interventions when necessary. Moreover, educational toys and tools connected to the IoT can facilitate interactive learning experiences tailored to a child's developmental stage, fostering cognitive and motor skills through engaging activities. The incorporation of IoT in these areas not only enhances children's well-being and safety but also provides parents with actionable insights and data-driven decision making, thereby supporting them in their caregiving roles. For newborns, crying is the primary means of communication. Even though baby cries seem to be similar to each other, they are physically generated and have distinctive features. Experienced carers such as parents, physicians, and nurses can identify these differences based on their previous interactions [7, 8]. However, it can be difficult for carers, new parents, and untrained paediatricians to interpret baby cries. Accordingly, it is imperative to differentiate between cries that have distinct meanings by utilizing associated auditory characteristics [9, 10, 11]. This can interpret a baby's needs and provide parents withappropriate ways to calm their infants. It can reduce stress for parents and other carers by helping them to avoid misinterpreting their infants' cries. Moreover, it can prevent infant mistreatment and neglect. Furthermore, analyzing an infant's screams provides a non-invasive way to evaluate their health without needing to perform invasive tests. In this situation, an automatic infant classification system helps medical personnel keep an eye on the baby's health and helps new parents understand their baby's needs based on their cries. One innovative application of IoT for neonatal care is the real-time monitoring and analysis of infant cries [12]. This process entails recording and labeling baby cry sounds, typically carried out by parents or trained medical professionals. Because infants and young children have limited control over their vocal tracts, their cry sounds are considered short-term stationary signals. However, the available infant cry datasets are relatively small, with most being self-recorded for research purposes and not publicly accessible due to the sensitive nature of the data collection and limited resources. As a result, some studies have turned to generating synthetic data for testing and training their methods [13]. Infant cry categorization is extremely difficult because of the available datasets' small size and class imbalance [14; 15; 16]. Dunstan Baby Language [17], Baby Chillanto Database [18], and donate-a-cryp Corpus [19] are a few of the datasets that are accessible in the literature. The cry recordings' duration, the classifications of cries they contain, and their sizes differ among these datasets [20]. The categorization of baby cries is typically a signal processing and machine learning task. In particular, it classifies the reason for crying using spectrogram image processing and machine learning approaches. In this context, research has been conducted [11; 21; 22; 23; 24; 25; 26; 27]. For instance, [PERSON] et al. 's study [23] focused on using four primary classes: hunger, attention, discomfort, and stomach problems. All other classes were grouped under the category \"Unidentified Reasons\". To investigate the recordings' acoustic properties, the authors extracted 20 audio variables, such as median/mean frequency, standard deviation frequency, kurtosis, spectral flatness, and more.