# Evaluating Convolutional Neural Networks and Vision Transformers for Baby Cry Sound Analysis
## 5 Threats to Validity  
In this section, we discuss potential threats to the validity of our research findings and the possible measures we took to reduce risks, whenever applicable. One threat to validity is sampling bias; our study used the donate-a-cryp corpus [19] as the primary dataset for cry sound analysis. While it provides a crowd-sourced collection of cry recordings, it may only partially represent the diversity of cry sounds encountered in real-world scenarios. Sampling bias in the dataset could impact the generalizability of the used models. In this context, we plan to experiment with other cry datasets mentioned in Section 1.  
A key challenge in our study was using a limited crowd-sourced cry sound dataset. However, we systematically mitigated this limitation through data cleaning, augmentation, and robust preprocessing. These strategies allowed us to harness the full potential of our dataset, resulting in reliable and meaningful cry sound classification models. Despite the dataset's constraints, our study's findings are promising, providing valuable insights into the ability of transformer architecture to classify cry sounds. When generalizing the findings, it is important to acknowledge these limitations, and further research with larger datasets could improve the accuracy of our models and confirm our findings.  
There is a threat to validity since the experiments performed for cry classification in previously published research on the donate-a-cry dataset do not use the original data directly. Therefore, the results of different research projects related to cry classification using the donate-a-cry dataset cannot be generalized. Various research works attempt to apply different data augmentation techniques to increase the number of samples to enhance prediction in the donate-a-cry dataset [19]. In [PERSON] et al.'s study [23], for the training process, the authors included all data from the donate-a-cry dataset [19], and an additional 150 baby crying audio samples were recorded and labeled for fine-tuning and testing purposes. Further, various versions of the donate-a-cry dataset exist due to the different extraction methods used by different authors. For example, for his analysis, [PERSON] [27] selected only recordings of infants aged between 0 and 6 months free from environmental noise. Each of these recordings was segmented into one-second intervals with a 50% overlap. This process yielded 1987 samples and then was divided into training and testing sets. These versions are independently compiled; we contacted the original creators to verify their availability but, unfortunately, had no luck. To address these constraints and ensure research reproducibility, we have made our code publicly available ([[https://github.com/[PERSON]/baby-cry-analysis](https://github.com/[PERSON]/baby-cry-analysis)]([https://github.com/[PERSON]/baby-cry-analysis](https://github.com/[PERSON]/baby-cry-analysis)) (accessed on 1 June 2024)). This includes steps for data cleaning, augmentation, and the generation of the 1252 samples utilized in our study, enabling others to reproduce our experiments and results.  
Additionally, each cry dataset contains different types of cry classes. Focusing on the donate-a-cry dataset [19], some of the research in the literature uses only a subset of four cry classes from the dataset. In comparison, others use the five main cry classes defined in the dataset, as summarized in Table 3.