## 1 Introduction
In these studies, the wave from the source is altered during propagation and this aspect is very rarely taken into account. Indeed, the signal is modified during propagation (mode conversion, reflections, dispersion) and then by the acquisition system [27; 28; 29; 30; 31; 32; 33; 34; 35; 36]. [PERSON] et al. [30; 31] show that the separation distance between the sensors is of paramount importance and it should be taken into account when crack mode estimation is attempted by experimental data. [PERSON] et al. [32] carried out impact tests on 260 mm outer diameter pressure vessels made of a fibreglass/epoxy matrix composite. Their study focused on the effect of source/sensor distance. They are able to show that the characteristic parameters of waveforms such as amplitude, rise time, as well as the spectral content of the signals for identical sources are largely influenced by the sensor source distance. For small distances (less than 60 mm), the physical significance of an acoustic event can be evaluated only by taking into account the propagation. Thus, the same author [33] showed that the representations obtained by the continuous wavelet transform vary according to the type of AE source and the propagation distance between these sources and the sensors in aluminium plates. The analysed parameters are highly dependent on the material properties, the structure geometries, the sensor and the detection and analysis system. In addition, the state of damage of the material can affect AE signals [34; 35]. All these evolutions make the interpretation of the signals very difficult. The specific values of the AE parameters are very sensitive to the experimental set up and conditions like geometries. Comparisons should be careful and only for exactly the same experimental conditions. In this context, the acoustic signature of a damage mechanism is not generalizable. The geometry of the sample is also an important parameter. Consequently, identification of the source and comparison with results from other tests performed under different conditions are difficult. The influence of the mentioned experimental conditions should be investigated and taken into account to increase reliability. The objective of the paper is twofold (1) use artificial sources with acousto-ultrasonic (AU) technique to generate AE sources with specific characteristics in order to analyse the sensor response and (2) study the influence of the sensor, propagation and damage on AE features and clustering results. Therefore, the first part of this paper is devoted to the study of the response of the sensor for artificial sources generated using an ultrasonic card. The AU technique consists in the excitation of the sample by a particular wave packet emitted from a sensor and the signal is recorded using an AEsystem. Artificial sources with different characteristics are created in undamaged state and during a mechanical test. [PERSON] et al. [37] develop a testing protocol that combines acousto-ultrasonic and acoustic emission monitoring, thus providing both global and local definite information on damage modes of SiC\({}_{\text{f}}\)/SiC minicomposites. Wave velocity measurements using acousto-ultrasonic allowed accurate location of AE sources by taking into account the damage dependence of wave velocity. In this study, the evolution of the waveforms as a function of the input characteristics and damage are investigated in order to guide pattern recognition techniques or the lifetime prediction based on the recorded energy. The second part is dedicated to the influence of the type of sensor and the source-sensor distance in order to highlight the limitations of the identification of the acoustic emission signature of several damage mechanisms. The present study uses various sensors to investigate the effect of the sensor and the propagation distance. Four sensors monitor the same tensile test on several kinds of composite in order to point out the influence of the sensor and its position.