## 1 Introduction  
Acoustic emission (AE) has become a widespread method of nondestructive testing, condition monitoring and structural integrity assessment for timely maintenance of dynamic mechanical systems (bearings [1; 2; 3; 4] and gearboxes [5; 6; 7]) and quasi-statically loaded industrial facilities (pressure vessels [8; 9; 10], pipelines [11], etc.). In general, AE transient displacements arise at the surface of the solid body in response to rapid structural rearrangements occurring locally during plastic deformation, fracture (including environmentally assisted degradation) or phase transformations. The process of AE generation at the source in structural metals and alloys bears a lot of similarity to that of the earthquakes and is driven by local elastic stress relaxations [12; 13]. As damage proceeds, AE appears as a random time series comprising of arbitrarily spaced individual pulses of different amplitudes and shapes, depending on the properties of the source. The amplitude of AE bursts can vary by several orders of magnitude in the dynamic range exceeding 100 dB. Pulses with a high signal-to-noise ratio (SNR) are easily detectable by any of the existing methods. As opposed to this, small amplitude signals are often not resolved from background noise and are therefore lost for any analytics. On the other hand, if the trigger level is set too low, false alarms are recorded due to noise fluctuations. Unidentified false triggering burdens the data analysis and can potentially result in dangerous misinterpretations and wrong decision-making. In fact, the AE sensitivity and the dynamic range are lower and bound by the inevitable background electronic noise that poses the main challenge for low amplitude signal detection. The AE analysis--like the analysis of a seismic time series--starts from signal detection, which is of paramount importance for further processing. Various data processing and onset picking algorithms have been proposed in the literature to minimise the localisation error and determine the most likely location of the source in different geometries [14; 15; 16; 17]. Plenty of trigger algorithms have been developed and used in seismology and to a much lesser extent, in the specific AE field. The simplest (and the \"oldest\") method is the amplitude-based threshold method [18], where an event is marked when the pre-set amplitude threshold is exceeded by the signal from the pre-amplifier output. Variations of this method include the envelope and/or root-mean-square threshold triggers, which are less sensitive to spike noise but are rarely used in practice. Although this method has been successfully used in many applications where the SNR is considerably high (e.g., \(>\)10 dB), its accuracy deteriorates rapidly as the SNR reduces [19]. A common in the AE field practice of counting the AE activity as a number of AE events per unit time or a total number of AE events suffers particularly strongly from the arbitrariness of the threshold setting, resulting in irrecoverable errors in AE data and their interpretations. Despite its obvious limitations associated with poor detection of the first arrival and incapability to detect the low amplitude events [14; 18], the fixed threshold is still the only one implemented in the existing commercial AE apparatus and is, therefore, the most widely used triggering method. The more precise and sophisticated schemes evolved in AE and seismic analysis include a variety of adaptive methods, pattern recognition, and neural network-based approaches. These methods are conventionally based on the features of the signal measured in time or frequency domain or using a more extensive set of features extracted in the time-frequency space. Among the time-domain approaches, the Short Time Average/Long Time Average (STA/LTA) technique [20; 21] or/and its modified versions [22] (see [23] for comparison) remain to be the most widely used method in seismology for decades. Multiple modifications of this algorithm have been reviewed in detail in [22; 24].