## 4 Performance Evaluation
### Results of Knowledge Transfer between the Apartment Buildings  
Table 5 presents evaluated performance of the knowledge transfer tasks with \(t\) variation, as explained in Section 3.4.3. The first eight rows present results of the source type knowledge transfer tasks \(\mathcal{T}_{\text{t,APT II|APT 1}}\) and \(\mathcal{T}_{\text{t,APT I|APT 2}}\). Their performance was evaluated with F1 scores, the values in parentheses are F1 scores when HH and HD are assumed to be the same category. The source type knowledge transfer results showed lower F1 scores than those of the cross-validation and test tasks. This performance degradation may originate from the domain difference. The underlined values in the remaining rows present F1 scores of the localization knowledge transfer results of \(\mathcal{T}_{\text{p,APT II|APT 1}}\) and \(\mathcal{T}_{\text{p,APT I|APT 2}}\). They were obtained by rearranging the localization results to their corresponding floors. As illustrated in Figure 6d, these F1 scores degraded as \(t\) increased. These may be explained by the difference of the reverberation characteristic between the inter-floor noise obtained in the two apartment buildings. Therefore, when target environment (apartment building) is changed, using short and early part of inter-floor noise is effective for localization with the source domain knowledge. Although the F1 scores of the results are lower than those of the cross-validation and the test tasks, they are considered to be meaningful, because they are better than the chance level, and demonstrate the feasibility of source type and localization knowledge transfer between the actual apartment buildings. The performance degradation is common for the one-dimensional CNN-based locator with \(t=0.152\) and \(0.501\) s.  
The feasibility of the knowledge transfer can be considered, such that a CNN can extract the generalized feature representations of source types and positions from inter-floor noises. Inter-floor noise filtered by learned one-dimensional CNNs with \(t=1.00\) s for source type classification and localization were embedded in a two-dimensional space using a dimension reduction algorithm t-stochastic neighbor embedding (t-SNE) [69] for visualization of the generalized features. Because the dimension of fc2 depends on the number of categories (\(n\)), output from fc1 were adopted in this case. Figures 7 and 8 present the t-SNE of the inter-floor noises filtered by the CNNs. Figure 7a,b illustrate the t-SNE of source type features in \(\mathcal{T}_{\text{t,APT II|APT 1}}\) and \(\mathcal{T}_{\text{t,APT I|APT 2}}\), respectively. The t-SNE of the same source types within the same apartment buildings are categorized into groups. In particular, the t-SNE of the same source types in different apartment buildings are clustered around the groups. This demonstrates that the CNN-based feature extraction can build generalized source type representations of the inter-floor noises. Figure 8 illustrates the t-SNE of floor features in \(\mathcal{T}_{\text{p,APT II|APT 1}}\) and \(\mathcal{T}_{\text{p,APT I|APT 2}}\). The t-SNE of fc1 are categorized into groups of above and below floors. However, the groups are not well clustered as the t-SNE of the source type features. It can be inferred that although the feature extraction of a generalized floor knowledge using CNN is feasible, but it is not significantly effective. The results in Table 5 also exhibit the corresponding values.