# Exploring Channel Properties to Improve Singing Voice Detection with Convolutional Neural Networks
## 4 Experiments and Results
### Multi-Scale Channels  
In this section, we demonstrate that the multi-scale channels strategy can improve the SVD performance and outperform the conventional single channel strategy. Before illustrating the multi-scale channels experimental results, we investigate the effects of the different scales of spectrograms on the performance. Figure 11 shows the boxplots of the accuracy performance on the three datasets with the different scales of spectrograms: 1024, 1536, 2048, 3072, and 3584. We described the experimental steps in the previous section. The reason for choosing these scales for the experiments is that their corresponding STFT windows are usually suitable for analyzing the music signal and can produce the reasonable results. It can be seen from the figure that the different scales can affect the accuracy performance to different degrees. On RWC, the scale of 1536 achieved the highest mean accuracy and was relatively stable. The scale of 2048 showed the best mean performance on JMD, and the scale of 1536 produced the best performance on Mir1k. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Method** & **Accuracy** & **F-Measure** & **Precision** & **Recall** & **FPR** & **FNR** \\ \hline \multirow{3}{*}{JMD} & CNN (\(\mu\)\(\pm\)\(\sigma\), \%) & 86.61 \(\pm\) 1.01 & 86.35 \(\pm\) 0.93 & 82.35 \(\pm\) 2.37 & 90.9 \(\pm\) 2.62 & 17.14 \(\pm\) 3.1 & 9.1 \(\pm\) 2.62 \\  & SDP-CNN (\(\mu\)\(\pm\)\(\sigma\), \%) & 88.78 \(\pm\) 0.75 & 88.38 \(\pm\) 0.68 & 85.55 \(\pm\) 2.07 & 91.49 \(\pm\) 1.99 & 13.58 \(\pm\) 2.5 & 8.51 \(\pm\) 1.99 \\  & SE-CNN (\(\mu\)\(\pm\)\(\sigma\), \%) & 88.82 \(\pm\) 0.81 & 88.48 \(\pm\) 0.72 & 85.21 \(\pm\) 1.9 & 92.07 \(\pm\) 1.56 & 14.02 \(\pm\) 2.26 & 7.93 \(\pm\) 1.56 \\  & CNN (\(\mu\)\(\pm\)\(\sigma\), \%) & 88.85 \(\pm\) 0.68 & 90.62 \(\pm\) 0.63 & 91.43 \(\pm\) 1.35 & 89.87 \(\pm\) 1.84 & 12.67 \(\pm\) 2.39 & 10.13 \(\pm\) 1.84 \\ \multirow{3}{*}{RWC} & SDP-CNN (\(\mu\)\(\pm\)\(\sigma\), \%) & 90.26 \(\pm\) 0.6 & 91.75 \(\pm\) 0.54 & 93.17 \(\pm\) 1.09 & 90.54 \(\pm\) 1.41 & 9.96 \(\pm\) 1.82 & 9.59 \(\pm\) 1.42 \\  & SE-CNN (\(\mu\)\(\sigma\), \%) & 91.13 \(\pm\) 0.52 & 92.49 \(\pm\) 0.5 & 93.88 \(\pm\) 0.92 & 91.17 \(\pm\) 1.45 & 8.92 \(\pm\) 1.54 & 8.83 \(\pm\) 1.45 \\ \multirow{3}{*}{Mir1k} & CNN (\(\mu\)\(\pm\)\(\sigma\), \%) & 88.91 \(\pm\) 0.23 & 93.73 \(\pm\) 0.13 & 90.67 \(\pm\) 0.43 & 97.01 \(\pm\) 0.54 & 58.67 \(\pm\) 3.31 & 2.99 \(\pm\) 0.54 \\  & SDP-CNN (\(\mu\)\(\pm\)\(\sigma\), \%) & 89.42 \(\pm\) 0.27 & 94 \(\pm\) 0.16 & 91.14 \(\pm\) 0.52 & 97.06 \(\pm\) 0.64 & 55.43 \(\pm\) 3.91 & 2.94 \(\pm\) 0.64 \\ \multirow{3}{*}{Mir1k} & SE-CNN (\(\mu\)\(\pm\)\(\sigma\), \%) & 89.67 \(\pm\) 0.24 & 94.13 \(\pm\) 0.14 & 91.47 \(\pm\) 0.61 & 96.97 \(\pm\) 0.72 & 53.17 \(\pm\) 4.55 & 3.03 \(\pm\) 0.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The experimental results on the datasets: JMD, RWC, and Mir1k. In addition to the single scale, we proposed that the multi-scale channels method can improve the overall performance with two or more scales of spectrograms. In order to validate our proposal, experiments using two, three, four, five, and six scales were conducted as follows. First, for each of the six numbers of scales, we created scale groups via combination. For example, the group containing two scales included the scales 1024 and 1536, 1024 and 2048, 1024 and 2560, etc. The total number of combinations in this group summed to \(C_{6}^{2}=15\). Then, on each dataset, we ran the experimental procedures two times with each combination of the scales in each scale group with the SE-CNN. For instance, for the number of scales \(i\), we ran the experiment \(2C_{6}^{i}\) times on each dataset. Although the number of results differed in the number of scales, it was sufficient to achieve a statistical comparison. Finally, all the experimental results are shown using the boxplots of Figure 12. For the single scale, we present the experimental results mentioned in the previous section.