## 1 Introduction
They have also used a parameter-based analysis of the acoustic emissions, which provided relevant predictive information about the material under study [29]. [PERSON] et al. studied the crack propagation process in grade-5 titanium samples subjected to uniaxial fatigue loading using AE and IR thermography. The AE techniques were found to be more accurate, as they were able to monitor crack activity and distinguish crack initiation in the beginning stages of the test [30]. Nonetheless, signal-based approaches are more efficient in comparison to parameter-based approaches, especially when the target is to assign the acoustic emission signals to their damage sources. Recently, researchers have developed approaches based on artificial intelligence [31], particularly the combination of the signal-based AE technique and artificial intelligence for damage characterization [32]. Some authors have implemented the artificial neural network (ANN) by using statistical descriptors for intelligent damage monitoring [33; 34; 35]. However, [PERSON] et al. and [PERSON] et al. stated that ANN is not suitable for capturing the content of information associated with AE signals [36; 37]. Currently, researchers are focusing on developing a deep learning approach using combined AE signals. The CWT is applied to AE signals, and the scalograms are extracted, containing the time-frequency. These scalograms are used as an image to be transmitted to a convolution neural network (CNN) for possible classification. The CNN is inspired by biological processes, which can automatically learn complex features during training and are much more computationally efficient than traditional neural networks due to the convolution process [38]. They can achieve a similar accuracy as human beings in image classification processes due to their self-learning capabilities to solve the multiple classification problem [39]. AlexNet is one of the most widely used neural network models to date.. It was first developed as part of the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC 2012) [40]. Its structure consists of five convolutional layers (Conv1, Conv2, Conv3, Conv4, Conv5), followed by three maximum pooling layers (Pool1, Pool2, Pool3) and two normalization layers (Norm1, Norm2). Two fully connected layers (Fc) are present before the final fully connected layer (Fc), leading to the output. SqueezeNet was proposed in 2016 by researchers at DeepScale, the University of California at Berkeley and Stanford University [38]. These networks are suitable for processing both large-scale and small-scale image inputs. The CNN and acoustic emission signals are used to characterize the damage mode of composite materials and structures. [PERSON] et al. worked on the classification of damage-induced acoustic emission signals in UHPC using convolutional neural networks [41]. Several CNNs are used--googleNet, ResNet18, efficientNet, and mobileNetV2--to explore the time-frequency characteristics of different damage-induced AE signals [41]. The results of the classification of AE signals showed that ResNet18 achieved the highest overall accuracy of 93.94% [41]. [PERSON] et al. successfully monitored the cracking-up scaling from specimen concrete structures using acoustic emissions and convolutional neural networks [42]. [PERSON] et al. proposed a deep learning approach based on acoustic emission data for the classification and detection of damage sources in a composite panel. They measured their AE signals through the piezoelectric sensor array. They used continuous wavelet transform (CWT) to extract time-frequency scalograms. They constructed a CNN to automatically extract the discrete damage features from the scalogram images [43]. The proposed deep learning approach has shown potential for effective damage monitoring with a high learning accuracy, and an image augmentation approach was proposed in their research work to generate training data [43]. [PERSON] et al.