## 2 Related Work  
Recent advancements in deep learning have significantly impacted AE source localization techniques. Single-sensor AE source localization, once challenging, has become feasible thanks to deep learning models [24; 25], showing particular promise in plate-like structures [22]. The application of these techniques extends beyond conventional materials, demonstrating efficacy in analyzing AE signals from nanoindentation experiments and environmental monitoring applications. However, current approaches face several limitations. A notable limitation is the ability to handle only a restricted number of zones or regions [26]. For example, in ref. [12], a plate was divided into just 13 zones for localization, likely insufficient for complex structures. While authors in [10] achieved better localization with 64 regions, its reliance on simulated edge-reflected waves and strong reflections limits broader application. The complexity of AE signals, which are highly susceptible to noise and environmental factors, further complicates data collection and interpretation [27]. To address these data limitations, researchers have begun exploring innovative approaches to enhance both the quality and quantity of available AE data. Deep learning techniques show promise in detecting acoustic wave reflections and improving signal interpretation [28]. Novel data augmentation techniques and physics-informed approaches are being developed to maximize the utility of limited datasets [29; 30]. Improved AE source localization methods, including time-distance domain transformations and wavelet-based noise reduction techniques, aim to extract more information from available signals [31]. Integrationof AE with other testing methods and advanced signal processing techniques, such as modified empirical wavelet transforms, is being explored to enhance damage detection capabilities [32]. Among these innovative approaches, Generative Adversarial Networks (GANs) have shown particular promise. GAN-based augmentation typically involves an encoder-decoder structure in the generator, where an input AE signal is encoded into a lower-dimensional manifold, combined with random noise, and then decoded to produce a new, related AE signal. The application of GANs in related fields has demonstrated their versatility and efficacy. In machine fault diagnosis, GAN-based data augmentation has yielded remarkable improvements in classification accuracy, particularly for fault types with sparse samples [33]. In medical imaging, GANs have been employed to generate synthetic radiology images, addressing issues of data scarcity and patient privacy [34]. Within SHM, innovative GAN architectures such as FTGAN have been developed to address imbalanced datasets in bearing fault diagnosis, incorporating both time and frequency domain information [35]. While GANs offer several advantages, including the ability to generate diverse, high-quality samples, learn complex data distributions, and improve model robustness, they also face challenges such as training instability, mode collapse (i.e., where the generator produces limited varieties of samples), and the potential to perpetuate biases present in the original dataset [36]. As the field progresses, addressing these limitations and adapting GAN architectures to the specific nuances of AE signals will be key to fully leveraging their potential. The fundamental GAN process involves the generator creating artificial samples from random noise while the discriminator struggles to differentiate between real and generated data, with both networks refining their performance through this adversarial process. Building upon these advancements and addressing the identified challenges, we propose a novel hybrid approach for AE source localization, combining a custom-designed GAN for data augmentation with an adapted Inception network for regression-based localization.