###### Abstract
In the past, for detecting and confirming water leaks, leak exploration, extraction, and recovery were carried out by leak detection experts to check for leaks and manage water flow rates. However, finding the exact location of leaks with audio leak detection is problematic because it can be affected by passing vehicles or surrounding noise due to the nature of the underground water supply pipeline. It also has the disadvantage of necessitating significant exploration skills on the part of workers and frequent visits to the site. In this paper, in order to detect and classify leaks in water pipes, we employ a 2D-CNN model that takes inputs consisting of spectral magnitudes of vibration sound samples. The sound data are sampled through the vibration sensors, which are mounted on the water pipes. The sampled data are collected from in situ water pipelines in several areas in Korea. They are classified into five categories by skilled experts. The leakage classes are indoor and outdoor sounds, while the other cases consist of typical sound, electrical, and mechanical noise, and environmental noise. The ultimate purpose of this research is to develop an intelligent detection model applicable to the automatic remote monitoring system of water pipes. Various performance measures are employed to investigate the model performance, and for the purpose of comparison, the support vector machine (SVM) model [27; 28] is also applied to the same dataset. As mentioned previously, approaches based on the CNN model show significant results in similar fields [19; 22; 23]. Furthermore, the CNN models can also guarantee real-time detection using already learned parameters [21]. For these reasons, the 2D-CNN model is employed. In addition to the CNN model, the SVM model is also adopted for performance comparison because several studies reported that the SVM model showed considerable performance for its application to leak detection [29; 30]. It is well-known that the computational efficiency of the CNN is maximized when its input is two-dimensional. For this reason, the magnitude spectrum, which is usually one-dimensional, is reshaped into a two-dimensional matrix for the input to the CNN model. However, since the SVM model can handle a one-dimensional array, it takes the one-dimensional magnitude spectrum as input. The SVM model in this study adopts the radial basis function (RBF) for its kernel. As leaks from water pipes produce noise, noise can be utilized to detect leaks [31], which is based on developing hearing (electronic) leak detection. For this reason, the dataset used in this study consists of the vibration data of the sound collected through the sensor installed in the water pipe and is the magnitude spectral density that has undergone the fast Fourier transform (FFT) process. In the existing leak detection methods, additional labor and costs are incurred, and extra time is required to identify if the alarm for leakage is genuine and localize leakage. However, installing the CNN model proposed in this study makes it possible to detect and classify what kind of leak and what kind of noise it is as soon as an abnormal sound is detected. The CNN-based water pipe leak detection model can save time and cost with high classification accuracy. The structure of the paper is as follows. In Section 2, the description of the data collection module and the collection places, and the detailed data constitution are introduced. Section 3 describes the CNN model employed in this study in terms of structure and training scheme. Section 4 introduces the structure, input characteristics, and hyperparameters of the SVM model. Section 5 presents the model training processes and the evaluation results. Finally, in Section 6, the experimental results are summarized, and the paper is concluded by presenting future works.