## 4 Results and Discussions
### Validation Setup and Parameters  
For each segment duration, 50 learning-testing iterations were conducted, each of which consists of the following four steps (Figure 4). First, the signals are segmented with the given duration \(T\) in the time domain. After segmentation, a fixed number of segments are randomly selected from the measurement data for training and testing. To make the training fair, the number of segments is kept constant for both the training and the test set, regardless of the segment duration. Without this measure, very short-duration segments yield a much larger sample size in the training set than long-duration segments, resulting in easier training and, thus, higher accuracy. Second, the selected segments are preprocessed to enhance the signal structure and format them into the same data size regardless of their duration. This is achieved by computing short-time Fourier transform (STFT) and averaging them over time. The averaged power spectral density is then standardized such that each has a zero mean and unit variance. Third, NCA is trained using the training segments to learn the optimal metric. This step is equivalent to supervised clustering. Lastly, the test segments, which are not used for training, are linearly transformed by the trained NCA and then classified using kNN.  
A summary of the validation parameters is given in Table 3. For the investigation, the range between 1 ms and 100 ms was selected for segmenting the recording of each patch. As discussed in Section 3.2, both ends of each patch were trimmed by 20 ms to eliminate the transitions, yielding 120 ms long recording for each patch. However, it is unclear how long the effect of the transitions lasts, and a longer segment such as 120 ms one may be affected by the transitions from both ends. In such cases, the performance comparison among different durations becomes unfair. To mitigate the effect of transitions as much as possible, an upper bound of 100 ms was introduced. Furthermore, no other processing techniques were applied to the signals aside from resampling them with the sampling frequency of 6 MHz prior to segmentation. To incorporate the possibly imperceptible information hidden in the high-frequency range, the entire frequency range of 3 MHz was used. For NCA clustering and kNN classification, the Python library scikit-learn [28] was used. The clustering and classification parameters were empirically selected by comparing the performance obtained using various values.  
Figure 4: Illustration of the validation procedure. (1) Signals are segmented with the given duration \(T\) in the time domain. (2) The segments are preprocessed in the frequency domain. (3) The preprocessed segments are clustered via NCA. (4) The test segments are classified via kNN.  
Strictly speaking, it is not fair to compare the performance among different sensor signals listed in Table 1, as these sensors have different sensitivity and characteristics. Nevertheless, comparing the performance among different sensor types can illuminate the advantages and drawbacks of different sensing methods. To highlight the informativeness of each sensor, the investigation of this study treats each sensor individually instead of fusing them. Possible benefits of sensor fusion are discussed based on the obtained results in Section 5. Note that the results of the structure-borne sensor placed at the end of the specimen (SB2) are omitted from the discussion in the subsequent sections. This is because they are found to be very similar to those of the other structure-borne sensor placed at the beginning of the specimen (SB1). Although sensor location does not have a significant effect on the informativeness of the signals of our study, a larger deviation will likely be observed in the results if the sensors are placed farther apart.