# Machine-Learning-Based Methods for Acoustic Emission Testing: A Review
## 1 Introduction  
Acoustic emission (AE) has long been recognized as a valid technique for real-time monitoring of materials and structures, providing useful information not only on the presence of defects but also on their criticalities [1]. Acoustic emission is a nondestructive test technique as it does not involve any input of energy into the materials. However, it should be noted that, to identify a fracture, it is necessary that this is already present in the material [2]. Nondestructive tests (NDTs) are a set of examinations, tests, and surveys conducted using methods that do not alter the material and do not require the destruction or removal of samples. The objective is to guarantee safety, verified in terms of compliance with the requirements of reliability and conformity to the project, according to which a specific product has been conceived and manufactured [3]. Many nondestructive methods have very narrow fields of application and great uncertainties of interpretation related to the influence of the conditions in which they are performed: the problem is often very complex, and clear information can only be deduced by comparing multiple results. Nondestructive methods can be useful for the complete and continuous monitoring of significant parameters over shorter or longer periods [4]. The identification of structural anomalies through sound emissions is a methodology that has successfully been applied for a long time. The potters, up to several millennia BC, were dedicated to listening to the audible sounds during the cooling phase to identify apossible structural defect. Similarly, a few millennia later, this procedure was applied in metalworking [5]. The artifacts were beaten to listen to the noise they emitted to identify possible fractures. In some artisan productions, these procedures are still applied today: think of the producers of bronze bells of the pondified foundries who are dedicated to listening to the sound produced by the bells to identify defects, or the expert producers of Parmigiano Reggiano, a famous Italian aged cheese, who identify the degree of ripeness of a wheel weighing about 40 kg by listening to the sound emitted by pressing with a small hammer. The bases of this technique, however, date back to the early 1950s when [PERSON] used an electronic instrumentation to detect the sound emissions emitted by metals during deformation [4]. [PERSON] found that all the metals investigated exhibited acoustic activity and that this activity was irreversible in the sense that it disappeared during a reloading process of the material until the stress level exceeded its previous value. This phenomenon is now known as the Kaiser effect and has proved to be of considerable use in studies with AE [6]. [PERSON] [7] and [PERSON] [8] began the research in the mid-1950s, making a significant contribution to the improvement of the instrumental equipment and to the clarification of the genesis of the AE. They were the first to observe that the emission in the metals was mainly due to the motion of the dislocations that accompanies the plastic deformation, rather than being entirely due to the reciprocal sliding of the grains, as initially proposed by [PERSON]. During the 1960s, many scholars dealt with AE using this technique in materials studies, characterization and quality assessment, nondestructive tests, and structural checks [9]. The remarkable advances in instrumentation, achieved in the early 1960s, made possible unexpected developments in AE technology. Researchers found significant difficulties in processing the AE signal due to background and ambient noise: many of these problems could be reduced, if not eliminated, by working in a frequency range well above the audible. This innovation eliminated the need for acoustically insulated laboratories and allowed for a high degree of perfection and applicability. Subsequently, in the early 1970s, it was decided to extend the concepts of spectroscopic analysis to acoustic phenomena originating from the dynamics of materials.