## 3 Methodology
### Noise Pattern Identification  
In this study, we utilized a clustering approach to identify distinct noise types in the driver's cabin of the metro system. This method involves comparing the frequency spectra and intensity levels of different noise clusters with those documented in existing literature to infer the potential noise sources. By correlating these clusters with known noise sources, we could efficiently categorize them without extensive sensor deployment across the train. Specifically, we employed the DBSCAN (density-based spatial clustering of applications with noise) algorithm to discern noise patterns in the driver's cab [49]. The noise data are usually characterized by its high dimensionality, diversity, and irregular distribution. Traditional clustering algorithms, such as k-means or hierarchical clustering, predominantly rely on Euclidean distances or other rudimentary distance measures, rendering them suboptimal for the nuanced handling required by high-dimensional data. Conversely, DBSCAN defines clusters based on density, thereby facilitating the identification of noise clusters of arbitrary shapes and sizes. This algorithm eschews reliance on predefined distance metrics, opting instead for local density to delineate cluster boundaries, thereby enhancing its robustness in identifying noise patterns. Furthermore, the noise data from the driver's cab frequently include anomalous high-intensity noise and background noise, often stemming from sporadic incidents (e.g., track impacts) or equipment malfunctions. Traditional clustering methods tend to erroneously incorporate these outliers within a cluster, thus compromising the integrity of the clustering results. DBSCAN, however, excels in recognizing these anomalies as noise points (i.e., outliers), effectively excluding them from any cluster. This feature ensures the purity of the clustering outcomes, rendering the identified noise patterns more precise and representative. Moreover, the driver's cab noise is an amalgamation of multiple noise sources. Traditional clustering methodologies like K-means necessitate the predefinition of the number of clusters (noise sources), an impractical requirement in complex and indeterminate noise en \begin{table}
\begin{tabular}{c c} \hline
**Metric** & **Description** \\ \hline Leq & Average sound pressure level over a specific period \\ Lmax & Maximum sound pressure level over a specific period \\ Lmin & Minimum sound pressure level over a specific period \\ L10 & Sound pressure level exceeded 10\% of the time \\ L25 & Sound pressure level exceeded 25\% of the time \\ L50 & Sound pressure level exceeded 50\% of the time \\ L75 & Sound pressure level exceeded 75\% of the time \\ L40 & Sound pressure level exceeded 90\% of the time \\ LR & Difference between the maximum (Lmax) \\ MF & Dominant frequency component over a specific period \\ TE & The total energy of the signal over a specific period \\ NE70 & Number of events exceeding a specific sound level (70 dB) \\ NE75 & Number of events exceeding a specific sound level (75 dB) \\ NE80 & Number of events exceeding a specific sound level (80 dB) \\ \hline \end{tabular}
\end{table} 
Table 2: The noise assessment metrics. vironments. DBSCAN obviates the demand for a preset cluster count, utilizing parameters \(\varepsilon\) (neighborhood radius) and minPts (minimum number of points) to adaptively uncover the actual number of clusters present in the data. This adaptability empowers DBSCAN to adeptly identify distinct noise patterns in uncertain noise data. Specifically, the core of the DBSCAN algorithm is based on the concept of the \(\varepsilon\)-neighborhood.