## 1 Introduction  
Pipelines are essential for transporting and distributing liquids and gasses over long distances in smart cities. However, they experience leaks due to corrosion, fatigue cracks, earthquakes, material defects, and environmental factors [1, 2]. These leaks can disrupt fluid supply systems, release hazardous materials into the environment, lead to high maintenance and repair costs, cause system downtime, and result in serious accidents. According to [PERSON] et al. [3], many countries heavily rely on transporting oil and water over long distances from desalination plants. However, a significant amount of these resources are lost each year owing to pipeline leaks, with up to 60% of water wasted annually [4]. To maximize pipeline efficiency, a reliable leak detection system (LDS) is important. An effective LDS should quickly detect leaks, minimize false alarms, be easy to install, perform well under various conditions, and have low-maintenance costs. This study proposes an LDS that uses acoustic emission (AE) signals and deep learning (DL) algorithms. In recent years, various techniques have been developed for externally and internally detecting pipeline leaks. These methods include accelerometer-based approaches [5], ultrasonic technology [6], acoustic emission (AE) technology [7], distributed temperature sensing systems [8], negative pressure wave techniques [9], time domain reflectometry [10], and magnetic flux leakage methods [11]. Among these, AE technology stands out for its ability to quickly detect leaks and provide a real-time response while exhibiting high sensitivity [12]. Considerable research on AE technology has focused on feature extraction and pattern recognition to create effective leak detection models [13; 14; 15; 16]. Given the prevalent use of AE-based monitoring in the industry, this study used AE technology to detect pipeline leaks. When a leak occurs in a pipeline, it generates elastic energy, creating an AE event. AE sensors mounted on the surface of the pipeline detect these events as acoustic emission hits (AEHs). The leak-induced AEHs cause variations in the AE signal [17]. Researchers have leveraged these signal variations to detect pipeline leaks, focusing primarily on the extraction of features [18; 19]. These methods can be categorized into the time domain, frequency domain, and multi-resolution time-frequency domain (TFD). For example, [PERSON] [20] proposed a pipeline leak detection approach that relies on statistical features in the time domain. The AE signals are initially preprocessed, and features are extracted in the time domain to obtain low-dimensional discriminant features. These features are further refined using principal component analysis. Ultimately, the pipeline operating condition is determined using a support vector data descriptor (SVDD). Moreover, pipeline leaks have been identified by analyzing the time domain AE signal amplitude using artificial neural networks (ANNs) [21]. However, interference noise and signal attenuation significantly impact AE signals, complicating leak detection in fluid pipelines when using time domain analysis. The frequency spectrum is more sensitive to variations in AE signals caused by leak-related AE events [22; 23]. [PERSON] and [PERSON] et al. [24] detected leaks in fluid pipelines by examining the changes in the amplitude of leak-related frequencies. The accuracy of leak detection was further improved using ANNs. In [25], various features are extracted from the AE signal in both time and frequency domains, including mean, mean frequency, skewness, crest factor, median frequency, kurtosis, root mean square, and peak frequency slope. They combined these features into a single feature vector and then selected leak-related features using the Kullback-Leibler distance. These features were classified using ANN, SVDD, and random forest to identify the pipeline leak state. Given the complex and non-stationary nature of AE signals from pipelines, frequency domain analysis is typically better suited for stationary signals [26; 27].