# Exploring Channel Properties to Improve Singing Voice Detection with Convolutional Neural Networks
## 4 Experiments and Results
### Channel Attention
#### 4.3.2 The Experimental Results  
In this section, the experimental results are presented to prove the effectiveness of our two proposed channel attention methods. To evaluate the results, we provide six metrics, i.e., Accuracy, F-measure, Precision, Recall, False Positive Rate (FPR) and False Negative Rate (FNR). Moreover, we provide the statistical rather than the optimal results because the CNN results are stochastic to some extent. The performance of the baseline architecture on JMD was reported in [22], in which Accuracy, F-measure, Precision, Recall, FPR, and FNR were 86.8, 86.3, 83.7, 89.1, 15.1, and 10.9 percent, respectively. However, the statistical results, for example, mean and standard deviation, were not presented. In order to provide more comprehensive performance results, we implemented the baseline architecture and the proposed architecture on the Pytorch platform, and conducted the experiments on the three datasets: JMD, RWC, and Mir1k. Furthermore, on each dataset, we trained, validated, and tested the three networks six times, with each of the scales (1024, 1536, 2048, 2560, 3072, and 3584) to achieve a better evaluation, i.e., run 36 times. Finally, we obtained the statistical results shown in Table 3, in which \(\mu\) and \(\sigma\) refer to the mean and  
Figure 10: Comparison between the input and the output feature maps of a scaled dot-product attention module. (**a**) An input image from the log-mel spectrogram of the music “you are”, (**b**) the input feature map, (**c**) the attentions or the weights of the corresponding channel of the output feature map (**d**).  
standard deviation of the 36 results, respectively, and SDP-CNN refers to the scaled dot-product channel attention-based CNN, and SE-CNN refers to the squeeze-and-excitation channel attention-based CNN. From the table, there is very little difference in the mean performances on Jamendo for the baseline CNN between our implementation and the implementation of [PERSON] et al. in [PERSON] et al. (2021), which indicates our implementation is believable.  
It can be seen from the table that the proposed channel attention method improved the performances on all the datasets compared to the conventional CNN, whereas SE-CNN slightly outperformed the SDP-CNN. Moreover, the proposed two methods are more stable on the two bigger datasets, i.e., JMD and RWC, because they present the smaller variances. In terms of stability, the SDP-CNN is on par with the SE-CNN due to the insignificant differences in the variances of the metrics on the three datasets. In terms of F-measure, the most significant increase was achieved on JMD by SE-CNN, with 2.13 percent, whereas the smallest increase was on Mir1k by SDP-CNN, with 0.27 percent. In fact, the overall performance increase on Mir1k was the smallest among the three datasets. We think that Mir1k is the \"easiest\" dataset and its potential is almost completely exploited by the inferior method, i.e., the baseline CNN, so that the better methods cannot significantly improve the results. In addition, the FPR values for Mir1k are very high because of the unbalanced samples, even though we used the weighted binary cross entropy as the loss function.