# Open-Set Recognition of Pansori Rhythm Patterns Based on Audio Segmentation
## 6 Results
### Identification of Unknowns by Temporal Segmentation for Other Audio Data  
#### 6.2.1 Result of Synthetic Instrument-Sound Data  
Table 7 delineates the performance metrics of our model for identifying unknown classes within synthetic instrument-sound data, prepared as described in the synthetic temporal stream depicted in Figure 4. To evaluate the model's effectiveness, drum sounds were added as an unknown class, and synthetic data were generated for testing purposes. Each synthetic chunk was 120 s in length, corresponding to a log-Mel spectrogram dimension of 3840 \(\times\) 32.  
The data reveal that the model's capability to identify unknown classes varies significantly with the depth and structure of the DNN. HRNet64, even in its basic form (without center loss), delivered the highest AUROC and AUPRC, scoring 93.0% and 69.4%, respectively. Furthermore, the incorporation of center and contrastive losses led to a consistent enhancement in performance, yielding an average improvement of approximately 5% in AUROC and 17% in AUPRC. It is obvious that different capability for identifying an unknown class can be obtained according to the depth and structure of the DNN. HRNet64 provided the highest AUROC and AUPRC for unknown detection in the basic approach (w/o center), as amounts of (93.0 and 69.4)%, respectively. In addition, the center and contrastive loss continuously increased the performance and achieved an average improvement of around 5% in AUROC and 17% in AUPRC.  
Figure 8 presents the segmentation results of the synthetic instrument data when the normalized threshold, \(\rho\), was set at 0.86. Interestingly, while the network accurately predicts known class frames in most instances--with accuracy as high as 88.8%--it struggles to delineate precise boundaries between different instruments, with noticeable position shifting. We hypothesize that this issue arises from the silent intervals between notes, during which  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Model Parameters** & **AUROC\(\uparrow\)** & **AUPRC\(\uparrow\)** \\ \hline Ours & 42.91 M & 0.816 & 0.472 \\ FFN [46] & 45.10 M & 0.733 & 0.366 \\ PSP [47] & 43.31 M & 0.687 & 0.407 \\ Unet [20] & 51.51 M & 0.697 & 0.323 \\ DeepLabV3 [23] & 58.62 M & 0.681 & 0.418 \\ DeepLabV3+ [22] & 45.66 M & 0.656 & 0.409 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The performance comparison of the traditional segmentation approach.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Baseline**} & \multicolumn{2}{c}{**w. Center Loss**} & \multicolumn{2}{c}{**w. Center, Contrast Loss**} \\
**Backbone** & **AUROC** & **AUPRC** & **AUROC** & **AUPRC** & **AUROC** & **AUPRC** \\ \hline HRNet32 & 0.761 & 0.391 & 0.836 & 0.502 & 0.859 & 0.604 \\ HRNet48 & 0.910 & 0.612 & 0.923 & 0.691 & 0.948 & 0.836 \\ HRNet64 & 0.930 & 0.694 & 0.942 & 0.762 & 0.950 & 0.840 \\ ResNet101 & 0.858 & 0.596 & 0.915 & 0.674 & 0.917 & 0.713 \\ WideResNet101 & 0.888 & 0.596 & 0.931 & 0.721 & 0.951 & 0.813 \\ ResNet152 & 0.902 & 0.637 & 0.927 & 0.698 & 0.941 & 0.774 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The performance of open-set instrument sound detection in various encoder networks.  
the model finds it challenging to attribute boundary frames to specific instruments. Additionally, this uncertainty in boundary delineation results in lower probability predictions, contributing to misclassifications of unknown frames, as illustrated in Figure 8d. At the set threshold, the precision and recall rates are 0.67 and 0.70, respectively.