# Exploring Channel Properties to Improve Singing Voice Detection with Convolutional Neural Networks
## 5 Conclusions  
In terms of CNN channels, we investigated two means to improve SVD performance and evaluated the results on three datasets. For channel attention, we proposed two methods: the scaled dot-product and squeeze-and-excitation, to measure the importance of the channels. It can be concluded from the related experimental results that the two channel attention mechanisms can be utilized for the CNNs to pay attention to the specific features in order to achieve better performance. In addition to raising the performance, the attention layer has the characteristic of \"plug and play\", which means the original CNN architecture does not need to be amended. In the proposed architecture, we only employed two attention layers. In the future, we will study whether the performance can be further improved by adding more attention layers. In addition, the experimental results showed that the FPR values were relatively high on the unbalanced dataset, Mir1k, despite using the weighted binary cross entropy as the loss function. Therefore, addressing the unbalanced data problem will be another research topic in the next stage. For the multi-scale channels, we proposed to combine the multi-scale spectrograms and feed them to the CNN at the same time, from which the CNN can automatically find the appropriate characteristics from the different representations. The experimental results provided a positive performance improvement. Specifically, the accuracy of the four scales channel was increased by 1% on JMD compared to the previous relatively high level. Essentially, this invokes the question of which scale of the spectrogram can represent the original signal in the most appropriate manner. In this study, we proposed to answer this question using multi-scale input channels and by letting the CNN achieve the task by learning. However, a deterministic solution may perhaps be found if a relation can be established between the appropriate scale and the tempo of the song, because the durations of notes can determine the scale. Moreover, the original signal can be fed to the DNNs to achieve the appropriate representation by feature learning because the original signal can provide the information of all the scales. This approach is like that used in the end-to-end approach applied in the field of singing voice separation [37]. Finally, we expect that these methods can inspire and benefit the other tasks in the field of MIR, in addition to the field of speech recognition. Although these proposed  
Figure 13: TCC of the SE-SNN and the SDP-CNN (**a**), and the increase in TCC with the change in the number of scales (**b**). approaches are \"plug and play\" modules for the CNNs, the number of input channels should be increased in the multi-scale channels scenario. Conceptualization, W.G. ; methodology, W.G. ; software, W.G., X.Z. and Y.L. ; validation, W.G., X.Z. and J.Z. ; formal analysis, W.G. and J.Z. ; investigation, W.G. and X.Z. ; resources, Y.L. ; data curation, W.G. and Y.L. ; writing--original draft preparation, W.G. ; writing--review and editing, Y.L. and J.Z. ; visualization, W.G. ; project administration, W.G. ; funding acquisition, W.G. All authors have read and agreed to the published version of the manuscript. This research was funded by the Open Research Fund of Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications), Ministry of Education, and by the Chinese National Natural Science Foundation, grant number 61872199, and by the Jiangsu Overseas Visiting Scholar Program for University Prominent Young & Middle-aged Teachers and Presidents, and by the High Level Project of the Educational Cooperation between Jinling Institute of Technology, Jiangsu, China and Queensland University of Technology, Brisbane, Australia. Not applicable. J.M. * [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON] A low-latency, real-time-capable singing voice detection method with lstm recurrent neural networks.