# Machine-Learning-Based Methods for Acoustic Emission Testing: A Review
## 1 Introduction
The development of ultrasonic techniques suggested the possibility that the excitability characteristic of materials, whereby a specific frequency response corresponds to an ultrasonic pulse, could justify a production of acoustic signals in the ultrasonic frequencies because of the release of energy at a microscopic level [10]. Modern acoustic-emission-based techniques have, therefore, neglected the audible spectrum to search for possible spectral signatures that identify structural defects in the ultrasonic spectrum. The term ultrasound defines elastic waves whose frequency is greater than the hearing limit for the human ear and whose frequency band, consequently, varies between 20 kHz and 1 GHz. Generally, in materials in which dynamic processes take place, such as deformations, fractures, or phase transitions, there is a release of elastic energy in the form of impulsive elastic waves, whose frequency spectrum is between 1 kHz and 10 GHz [11]. The dynamic processes at the origin of acoustic emissions are well-highlighted, for example, by the macroscopic acoustic phenomena related to the breaking or deformation of a solid material. The physical interest in these phenomena, however, concerns signal far from the limits of the audible spectrum, both in intensity and in frequency, determined in solid materials by the motion of the displacements, growth of microfractures, and motion of the grain thread, processes that we will call emission sources acoustics [12]. A solid material, subjected to a stress that sets it in vibration, will undergo fractures at the molecular or atomic level, and a certain energy will be released. Furthermore, a voltage distribution will develop, which will depend on the phenomenon as a whole and will involve the whole system considered. As soon as this distribution of tension crosses an area in which breaks have already occurred, it is possible that new ones will occur, since it is where the structure is weakest [13]. This implies a real chain reaction that will support the progress of each existing fracture, rather than the formation of new ones. Acoustic emissions are generated at a microscopic level due to breakage of the chemical bond and propagate because of a real chain reaction. We cannot grasp the effects of one of the breakings of a single chemical bond, but natural phenomena have memory of their origins and past events [14]. Machine learning (ML) is the technology for the development of computer algorithms capable of emulating human intelligence [15]. It draws ideas from various disciplines such as artificial intelligence, probability and statistics, computer science, information theory, psychology, control theory, and philosophy [16]. This technology has been applied in different fields such as pattern recognition [17], prediction of material characteristics [18], automatic recognition of acoustic sources [19], computer vision [20], prediction of perception personal to different stimuli [21], etc. The most important property of these algorithms is their distinctive ability to learn the surrounding environment from input data. The ability to learn through the input of the surrounding environment is the main key to developing an efficient automatic identification application. In this case, learning is defined as estimating dependencies on data [22]. To do this, ML algorithms are used to query large databases and discover previously unknown properties in the data. Many ML algorithms use unsupervised learning methods such as preprocessing to increase learning accuracy before examining the desired activities. These characteristics have suggested the use of such methodologies for the automatic identification of structural defects through acoustic emission techniques [23]. In this work, the most popular methods based on ML for AE testing were analyzed and described. The paper is structured as follows: Section 2 describes in detail the acoustic emission testing methods, analyzing their characteristics and peculiarities that make the mathematical modeling extremely complex.