## 5 Conclusions and Future Study  
In this study, the generalizability of the CNN-based supervised learning method for source type classification and localization of inter-floor noise was demonstrated via several designed tasks. Furthermore, the feasibility of source type and localization knowledge  
Figure 8: The symbols in purple and those in yellow represent t-SNE of learned and unlearned signals, respectively, for a single selected floor among floor above and below. In addition, those in gray represent the other floor. (**a**,**b**), respectively, colorize signals from the floor above and below in \(\mathcal{T}_{\text{P,APT\,||APT\,1}}\). (**c**,**d**), respectively, colorize signals from the floor above and below in \(\mathcal{T}_{\text{P,APT\,||APT\,2}}\). Figure 7: t-SNE of source type features filtered by (**a**) SoundNet \(f_{\boldsymbol{\Theta}_{\text{\tiny{GAPT}}}}(\cdot)\) in \(\mathcal{T}_{\text{\tiny{LAPT\,||APT\,1}}}\) and (**b**) SoundNet \(f_{\boldsymbol{\Theta}_{\text{\tiny{GAPT}}}^{i}}(\cdot)\) in \(\mathcal{T}_{\text{\tiny{LAPT\,||APT\,2}}}\). The symbols in purple and those in yellow represent t-SNE of learned and unlearned signals, respectively, for a single selected source type. In addition, those in gray represent the other source types. transfer between apartment buildings was demonstrated. These were demonstrated using inter-floor noise datasets obtained from two actual apartment buildings. The source type classifiers and locators consist of CNN-based feature extractors followed by an adaptation layers and a softmax function. Pre-trained weights on largely annotated image datasets were used for initialization of the feature extractors. Noise events in the signals were detected using the HOS-based algorithm and inputed to one-dimensional (SoundNet) and two-dimensional (VGG16) CNNs. The signal length in time \(t\) was selected empirically. The source type classifiers and locators were verified against several tasks, which are five-fold cross-validation on inter-floor noise transmitted through a learned floor section in individual two apartment buildings, test on that transmitted through an unlearned floor section in the same apartment buildings, and test on that obtained from unseen apartment buildings. The performance of the method for each task was evaluated using the F1 score. VGG16-based source type classifier and locator performed better than those of SoundNet-based. For cross-validation of the source type classification with \(t\) = 2.00 s, VGG16-based source type classifier showed F1 scores of 0.9731 and 0.9551 for APT I and APT II dataset, respectively. If HD and HH are considered the same category, these scores were improved to 0.9798 and 0.9953. For test of the prepared source type classification tasks using inter-floor noises transmitted from the unlearned source positions, F1 scores of source type classification results by the same classifiers dropped to 0.8303 and 0.7991, respectively. Furthermore, if HD and HH are considered the same category, the scores reached 0.9563 and 0.9875. For cross-validation of the localization, VGG16-based locator with t = 2.00 s showed F1 scores of 0.9574 and 0.9272 for APT I and APT II dataset, respectively. Rearranging these results to their corresponding floors modified F1 scores to 0.9955 and 0.9786, respectively. However, the method showed limitation to test tasks using noise signals transmitted through the unlearned floor sections. However, rearranged results to corresponding floors classification dropped less than 2% from the those of cross-validation tasks. These results present the generalizability of the identification method with a single sensor in an actual multi-storey reinforced concrete building and the feasibility of the knowledge transfer between similar buildings. In conclusion, results in this study contribute to identify inter-floor noise in real multi-storey apartment buildings and other single sensor-based approaches.