# Optical Medieval Music Recognition Using Background Knowledge
## 8 Results  
In this section, the setting for the performed experiments is described, as well as their interpretation and the experimental conclusions that can be drawn. First, an overview of the experiments is presented, for which the results in this chapter are reported:  
1. Evaluation of different encoder/decoder architectures within our complete OMR pipeline with data from the same dataset as the training data by a 5-fold cross validation (i.e., 80% of the data used for training, the remaining 20% for evaluation) with a large dataset (Nevers Part 1-3 + Latin 14,819 with 50,607 symbols; compare Table 1 (Table 4). \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Detection**} & \multicolumn{4}{c}{**Type**} & \multicolumn{4}{c}{**Position in Staff**} & \multicolumn{4}{c}{**Sequence**} \\  & **Archit.** & \(\mathbf{F_{1}^{all}}\) & \(\mathbf{F_{1}^{rate}}\) & \(\mathbf{F_{1}^{def}}\) & \(\mathbf{F_{1}^{facids}}\) & \(\mathbf{Acc_{note}}\) & \(\mathbf{Acc_{def}}\) & \(\mathbf{Acc_{note}}\) & \(\mathbf{Acc_{def}}\) & \(\mathbf{hSAR}\) & \(\mathit{dSAR}\) & \(\mathit{mAR}\) \\ \hline \multirow{11}{*}{2-15} & FCN & 97.9 & 98.1 & 93.0 & 23.0 & 97.7 & 99.0 & 98.8 & 99.8 & 94.4 & 92.4 & 84.6 \\  & MobileNet & 98.4 & 98.5 & 96.4 & 75.0 & 97.8 & 99.1 & 98.9 & 99.7 & 95.4 & 93.5 & 89.6 \\  & ResNet & 98.8 & 98.9 & 97.3 & 80.2 & 98.0 & 99.2 & 98.9 & 99.8 & 96.2 & 94.5 & 91.9 \\  & Eff-b1 & 98.7 & 98.8 & 97.0 & 87.6 & 98.3 & 99.1 & 98.9 & 99.8 & 96.3 & 94.7 & 91.7 \\  & Eff-b3 & 99.0 & 99.0 & 97.5 & 89.8 & 98.1 & 99.5 & 98.9 & 99.8 & 96.7 & 95.1 & 92.8 \\  & Eff-b5 & 99.0 & 99.0 & 97.8 & 89.7 & 98.2 & 99.5 & 98.9 & 99.7 & 96.7 & 95.1 & 93.6 \\  & Eff-b7 & 99.1 & 99.1 & 98.0 & **90.0** & 98.3 & 99.4 & 99.0 & 99.8 & 96.7 & 95.3 & 93.8 \\  & U-Net & 99.2 & 99.3 & 98.2 & 86.0 & **98.4** & 99.1 & 99.0 & 99.8 & 97.2 & 95.8 & 94.1 \\ \cline{2-15}  & Mean & 98.6 & 98.8 & 96.9 & 77.7 & 98.1 & 99.2 & 98.9 & 99.8 & 96.2 & 94.5 & 91.5 \\ \hline \multirow{11}{*}{2-15}  & FCN & 98.1 & 98.5 & 95.0 & 23.0 & 97.5 & 98.1 & 98.8 & 99.6 & 95.1 & 93.0 & 89.2 \\  & MobileNet & 98.5 & 98.6 & 97.0 & 74.4 & 97.4 & 99.0 & 99.1 & 99.8 & 95.8 & 93.8 & 92.5 \\  & ResNet & 98.9 & 99.0 & 97.8 & 80.2 & 97.7 & 99.1 & 99.1 & 99.8 & 96.7 & 94.8 & 94.2 \\  & Eff-b1 & 98.8 & 98.9 & 97.4 & 86.8 & 98.0 & 99.2 & 99.1 & 99.8 & 96.5 & 94.9 & 94.0 \\  & Eff-b3 & 99.1 & 99.1 & 97.7 & 88.6 & 97.8 & 99.3 & 99.1 & 99.8 & 96.7 & 95.0 & 94.4 \\  & Eff-b5 & 99.0 & 99.1 & 98.0 & 89.9 & 97.9 & **99.6** & 99.1 & 99.9 & 97.0 & 95.2 & **95.3** \\  & Eff-b7 & 99.1 & 99.2 & 98.1 & **90.0** & 98.0 & 99.1 & 99.0 & **99.9** & 96.8 & 95.2 & 94.7 \\  & U-Net & **99.3** & **99.4** & **98.2** & 87.0 & 98.0 & 99.3 & **99.2** & **99.9** & **97.3** & **95.7** & 95.1 \\ \cline{2-15}  & Mean & 98.9 & 99.0 & 97.4 & 77.5 & 97.9 & 99.1 & 95.0 & 99.8 & 96.5 & 94.7 & 93.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Preliminary experiments for the symbol detection using different network architectures. All results are the averages of a five fold of each dataset part. The used backbone is stated on the left. The results of the detection accuracy of all symbols, of solely the notes and the clef, the type accuracy (Notes: looped, gapped, neume start; Clef: C-Clef, F-Clef), the position in staff accuracy of notes and clefs and the \(\mathit{hSAR}\), \(\mathit{dSAR}\) and \(\mathit{melody}\), given in percent are computed using all datasets except the Pa904. The bold numbers highlight the best value for each metric. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Big Drop Capital** & **Small Drop Capital** \\ \hline TP & 18 & 15 \\ FP & 0 & 0 \\ FN & 1 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of the qualitative evaluation of the drop capital detection. For the following evaluations, the simple baseline architecture (FCN) and the most promising architecture (Eff-b3) were used. 2.