## 2 Data Description  
In this work, we used a dataset provided by AI Hub (This research utilizes the datasets from The Open AI Dataset Project, South Korea. All data information can be accessed through AI-Hub (www.aihub.or.kr) on 23 December 2022.) [32]. This dataset was collected from leak detection sensors installed in 11,000 locations in some neighborhoods in Gwangiu, Korea, and at the modernization site of the local water supply in Goheung, Korea. The raw data were obtained by monitoring water pipe leakage vibration using the monitoring system illustrated in Figure 1.  
Figure 2 shows how the sensor attached to the water pipe is utilized to discriminate indoor and outdoor leakage [32]. Indoor leakage refers to the case of leakage in the water supply pipe, where leakage noise is detected by the sensor attached to the pipe, as shown in Figure 2a. In contrast, outdoor leakage refers to the case of leakage in the drain pipe. Drain pipe leakage noise is propagated to nearby water supply pipes and the sensors equipped to supply pipes around the drainpipe detect the leakage simultaneously, as shown in Figure 2b. Therefore, in this case, the leak detection signal is simultaneously transmitted from sensors near the drain.  
The collected data are refined based on the decision confirmed in the field through actual leakage exploration and classified for subsequent analysis. Data types are largely divided into three types: normal, abnormal, and noise. The data are specifically labeled into five classes: outdoor leak, indoor leak, electrical/mechanical noise, environmental noise, and normal sound. The outdoor leak sound is the sound when a leak occurs in the drain pipe, and the indoor leak sound is the sound when a leak occurs in the water supply pipe. Electrical/mechanical noise is a sound mixed with electrical sound such as heating wires to prevent water pipes from freezing and bursting, and environmental sound is a sound mixed with life noise, such as the operation sound of an outdoor unit. For each class, 21,923 outdoor leaks, 16,591 indoor leaks, 6288 electrical/mechanical sounds, 8774 environmental sounds, and 24,628 typical sounds, for a total of 78,204 samples are the numbers of data samples used in the experiment were. The composition of the data is summarized in Table 1.  
The sensor signals are transformed into a spectral density using 1024-FFT, with a spectral range from 0 Hz to 5120 Hz. Each datum consists of an area code, a sensor number, and spectral magnitudes according to frequency. Figure 3 shows the average magnitude spectra for the five cases. In Figure 3a, the spectra of normal sound and indoor and outdoor leak sounds can be compared, while Figure 3b shows the noise sounds, which are classified by the regular classes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Label** & **Number** \\ \hline Normal & Normal & 24,628 \\ \hline Anomaly & Outdoor Leakage & 21,923 \\  & Indoor Leakage & 16,591 \\ \hline Noise & Electrical/Mechanical & 6288 \\  & Environmental & 8774 \\ \hline Total & & 78,204 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data composition summary.  
Figure 1: A flow and water pressure monitoring module was used to collect data [32].  
Figure 2: Detection scenarios. (**a**) Indoor water leak and (**b**) outdoor water leak.