# Singing Voice Detection in Electronic Music with a Long-Term Recurrent Convolutional Network
## 3 Experiments
### Comparison with Related Works on Existing Datasets  
As the last experiment, to validate the proposed SVD system, both datasets were used to perform 10 prediction results. From all of the evaluation metrics, the \(F1\)-measure provides the most information about the effectiveness of the model. Furthermore, the standard deviation was calculated to validate the consistency of the results. The model was set with the best parameters obtained on the previous experiments (vector of 276 components, block size of 20 frames, SVS as a preprocessing step). The averaged results are shown in Table 2.  
The SVD system performed very well on the Jamendo dataset, with an \(F1\)-measure of 0.942 and standard deviation of 0.004, whereas on the Electrobyte dataset it achieved an \(F1\)-measure of 0.828 and standard deviation of 0.006. To validate the robustness of the model, a K-fold cross-validation was performed on both datasets, in which the training, validation, and test sets were resampled seven times by randomly selecting the songs for each. Across all seven samples individually, the standard deviation for the averaged \(F1\)-measure on Jamendo was lower than 0.004, whereas for Electrobyte it was lower than 0.008. It is worth mentioning that by repeating the experiment several times, one of the samples performed better than the original set on both datasets, with an \(F1\)-measure of 0.966 on Jamendo and 0.885 on Electrobyte. However, when considering the results of the samples and the original dataset as a whole, the standard deviation for the averaged \(F1\)-measure on the Jamendo dataset was 0.016, whereas for the Electrobyte dataset it was 0.021. Although the performance of the system in Electrobyte was lower compared to Jamendo across all samples, as this is the first study evaluating SVD in electronic music, these results will serve as a framework for future studies about the intricacies of the genre and how to improve the detection of vocals with more effective models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **F1-Measure** & **Deviation** \\ \hline Jamendo & 0.939 & 0.937 & 0.945 & 0.942 & 0.004 \\ Electrobyte & 0.833 & 0.798 & 0.861 & 0.828 & 0.006 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of the SVD system on two different datasets.  
Figure 10: Performance of the LRCN when using and not using SVS as a preprocessing step.  
Finally, the SVD system was compared with the best value performance from [PERSON] [30], [PERSON] [12], [PERSON] [33], [PERSON] [34], [PERSON] [16], [PERSON] [17], and [PERSON] [11] on the Jamendo dataset. The comparison results are shown in Table 3.  
Table 3 shows the comparison of the results of the proposed LRCN on the Jamendo dataset against those obtained on Jamendo in related reports under the same conditions. When compared with the classifiers based on LSTM, the proposed LRCN achieved an \(F1\)-measure of 0.942, which outperforms the BLSTM of [PERSON] and LSTM of [PERSON] by 4%. Furthermore, it shows an improvement over the state-of-the-art method reached by the LRCN of [PERSON] by 1.5%.