## 6 Extended Experiments
### Evaluation
confusion between the documents due to the increased data set size. Another reason is the increased diversity of database documents. To gain more insights into these results, we now analyze the evaluation for each of the individual composers of the data set. In the following, we fix the dimensionality of \(K=12\) as a typical example that gives a good trade-off between retrieval quality and speed, as shown in the previous experiments. Table 8 shows the detailed evaluation results for \(K=12\). The rows correspond to the composers of the data set, and the last row reports the averaged evaluation measures over all queries and all composers. Note that composers with many queries have a stronger impact on this average compared to composers with few queries. The queries from the [PERSON] recordings result in an MAP value of 0.932 with the brute-force approach, 0.686 with PCA, and 0.825 with DNN. These numbers are lower than the ones we obtained for the smaller test set (0.972 for brute-force, 0.877 for PCA, and 0.928 for DNN; see Table 5). In particular, PCA suffers from using the larger test data set. The moderate loss for the DNN approach could point towards overfitting, because [PERSON] is also the most prominent composer of the training set. The queries from the [PERSON] recordings result in an MAP value of 0.940 with the brute-force approach, 0.755 with PCA, and 0.617 with DNN. Here, the DNN is considerably worse than the linear PCA-based projection. The late-romantic style of [PERSON] could be too different from the styles of the training set. The queries from the [PERSON] recordings result in an MAP value of 0.918 with the brute-force approach, 0.462 with PCA, and 0.553 with DNN. In this case, the DNN achieves better results than those of the PCA. Note that [PERSON] is not part of the training set and that the respective work is a piano concerto, which is a classical music genre that is also not covered in the training set. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Queries**} & \multicolumn{3}{c}{**Brute Force**} & \multicolumn{3}{c}{**PCA (\(K=12\))**} & \multicolumn{3}{c}{**DNN (\(K=12\))**} \\  & & **P@1** & **P\({}_{R}\)** & **MAP** & **P@1** & **P\({}_{R}\)** & **MAP** & **P@1** & **P\({}_{R}\)** & **MAP** \\ \hline [PERSON] & 160 & 0.963 & 0.827 & 0.879 & 0.631 & 0.456 & 0.521 & 0.669 & 0.477 & 0.538 \\ [PERSON] & 2930 & 0.996 & 0.876 & 0.932 & 0.899 & 0.626 & 0.686 & 0.936 & 0.765 & 0.825 \\ [PERSON] & 210 & 0.967 & 0.921 & 0.953 & 0.790 & 0.645 & 0.716 & 0.819 & 0.646 & 0.728 \\ \hline [PERSON] & 240 & 0.992 & 0.960 & 0.976 & 0.754 & 0.543 & 0.619 & 0.796 & 0.593 & 0.667 \\ [PERSON] & 200 & 0.950 & 0.920 & 0.940 & 0.845 & 0.682 & 0.755 & 0.700 & 0.539 & 0.617 \\ [PERSON] & 200 & 1.000 & 0.864 & 0.907 & 0.630 & 0.390 & 0.443 & 0.560 & 0.349 & 0.399 \\ [PERSON] & 660 & 0.967 & 0.834 & 0.877 & 0.718 & 0.482 & 0.545 & 0.642 & 0.452 & 0.516 \\ [PERSON] & 150 & 0.980 & 0.893 & 0.918 & 0.553 & 0.400 & 0.462 & 0.653 & 0.472 & 0.553 \\ [PERSON] & 420 & 0.983 & 0.892 & 0.925 & 0.729 & 0.493 & 0.566 & 0.621 & 0.492 & 0.543 \\ [PERSON] & 180 & 0.983 & 0.849 & 0.912 & 0.761 & 0.625 & 0.676 & 0.722 & 0.584 & 0.636 \\ \hline \(\diameter\) over queries & & 0.987 & 0.877 & 0.924 & 0.818 & 0.577 & 0.639 & 0.818 & 0.646 & 0.708 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Composer-wise evaluation results using the extended data set \(\mathbb{D}_{3}\). Figure 6: Evaluation results for the smaller test set \(\mathbb{D}_{2}\) (**a**) and the extended data set \(\mathbb{D}_{3}\) (**b**). A possible explanation for the poorer results of the embedding methods could be that the embeddings are just overfitted to the composers and styles of the training set and are not very discriminative in general. In the next section, we will question this argument by considering longer query audio fragments.