# Evaluating Convolutional Neural Networks and Vision Transformers for Baby Cry Sound Analysis
## 1 Introduction
Three clustering algorithms were used in their experiments: Gaussian mixture models, hierarchical clustering, and k-means clustering. Gaussian mixture models performed the best of the clustering models, showing the fewest overlapping points. [PERSON] et al. [24] similarly concentrated on four classes: fatigued, terrified, hungry, and uncomfortable. Several preprocessing procedures are used for cry signals in their suggested model. They first performed linear filtering to highlight high-frequency regions (i.e., High-order Spectral (HiSpec)). The SAI model was then used, which consists of peak picking, power-law compression, half-wave rectification, and a gamma tone filterbank. The next step was to extract logarithmic energy and linear prediction coefficients (LPCs) to model the envelope and amplitude. Ultimately, the researchers employed the Gaussian Mixture Model-Universal Background Model (GMM-UBM) for classification since it performed better than the support vector machine (SVM) model. In light of this, their research shows that the SVM model struggles with damaged signals but works well for small datasets. The more adaptive UBM, GMM-UBM, performs better at identifying baby cry signals than the SVM model. Under clear cry signals, however, there is a considerable difference in the HiSpec feature between SVM and GMM-UBM. By 14.6% and 13.7%, respectively, their suggested auditory model-based feature increases recognition accuracy. However, additional research into alternative techniques for extracting features from the auditory model remains imperative. Furthermore, the authors of [25] proposed a deep learning-based ensemble model that uses three different input types: MFCC features, STFT features, and face photos of infants in tears. Applying the donate-a-cryp corpus data [19], the authors validated the sound features retrieved using the suggested STFT and MFCC methods. They used a combination of autoencoders and deep neural network (DNN) algorithms to classify audio features. [PERSON] [27] recently investigated various methods for analyzing baby cries, including cutting-edge deep learning models and conventional machine learning models using man ually constructed features. Their analysis of the donate-a-cry dataset showed that the most promising outcomes came from separating crying signals into spectrograms and scalograms and then classifying the data using either ResNet-18 or Shuffle-Net. Scalograms with ResNet-18 and Shuffle-Net and spectrograms with ResNet-18 were their best-performing models. The use of pre-trained CNN models limits the usefulness of this work. Similar to this, the authors of [26] studied feature ablation using multiple spectral features and a range of classifiers, including Random Forest, SVM, KNN, and Logistic Regression, on recordings belonging to four categories: burping, hungry, belly pain, and discomfort. The most effective features, when combined with the Random Forest and K-Nearest Neighbor algorithms, were found to be Mel-frequency cepstral coefficients (MFCCs), Gamma Tone Frequency Cepstral Coefficients (GFCCs), and Zero Crossing Rate (ZCR). Most studies used convolutional neural network (CNN) architectures, neural network-based classifiers, and conventional machine learning classifiers that used hand-crafted features, according to an overview of the literature on baby cry classification. After reviewing the literature, we discovered that transformers and attention-based categorization techniques still need to be applied in these investigations [28]. Transformer architecture has been applied, adapted, and extended to various areas, including computer vision, audio processing, and more, following its breakthrough in natural language processing (NLP) applications. Transformers have also been used in the audio domain for various tasks, including audio categorization, speech recognition, and music production [29; 30; 31].