## 1 Introduction  
Acoustic emission (AE) is a well-established and powerful non-destructive testing method for structural health monitoring (SHM), offering unique capabilities for real-time damage detection and localization [1]. AE techniques analyze transient elastic waves generated by rapid energy release within materials, providing insights into structural integrity in a variety of structures ranging from wind turbine blades to aerospace components [2; 3]. The application of AE to source localization has changed damage detection and monitoring strategies, offering high sensitivity and the ability to detect defects in real-time [4]. Current research aims to develop robust, adaptive, and efficient SHM systems capable of reliably assessing the health of increasingly complex structures in demanding operational environments, focusing on enhancing data quality, improving localization accuracy, and developing more sophisticated predictive models [5; 6; 7].  
Existing AE data analysis methods employ signal processing techniques such as time-domain, frequency-domain, and time-frequency analysis to extract key features from signals [8]. Conventional approaches such as triangulation and beamforming, while widely used for source localization, face limitations in practical applications, particularly in the complex real-world environment. These techniques rely on the time difference of arrival (TDOA) of signals at multiple sensors and require precise knowledge of wave velocities [9]. Despite their effectiveness in certain scenarios, implementation can be challenging in complex real-world environments, particularly in larger structures or noisy conditions. In response to these challenges, single-sensor AE localization techniques have gained attention, offering more cost-effectiveness and simpler deployment. [PERSON] [10; 11] pioneered probabilistic frameworks for single-sensor AE source localization in thin metallic plates, relying on the temporal order of signal arrivals rather than absolute time measurements.  
In recent years, deep learning approaches have revolutionized AE data analysis, offering powerful tools for SHM and damage detection. These methods have excelled in source localization, signal classification, and characterization of acoustic events [12; 13; 14; 15]. Machine learning methods have been widely applied to structural health monitoring (SHM), transforming traditional approaches to damage detection, localization, and prognosis [16; 17]. These techniques have demonstrated significant potential in addressing SHM challenges, from vibration-based monitoring to predictive maintenance [18]. Additionally, advanced methods such as the Electro-Mechanical Impedance technique have shown promise in SHM applications, offering new avenues for damage detection and structural assessment [19; 20]. Despite these promising advancements, some challenges persist in deep learning for AE data analysis. A fundamental challenge is the limited availability of high-quality data, significantly impacting the development and performance of damage detection and localization algorithms. This scarcity of data stems from the rare and unpredictable nature of AE events, particularly in critical structural failures.  
To address data limitations, generative adversarial networks (GANs) have emerged as a powerful tool in data augmentation, offering significant potential for enhancing time series signal analysis. Introduced by [21], GANs comprise two neural networks--a generator and a discriminator--that compete in a zero-sum game, iteratively training each other to produce high-fidelity synthetic data [22]. In the context of AE signal augmentation, GANs present a promising solution to the persistent challenge of limited and imbalanced datasets. By generating synthetic AE signals that closely mimic real data characteristics, GANs can significantly expand training datasets, potentially improving the performance and robustness of damage detection and localization algorithms [23].