# Improving Singing Voice Separation Using Curriculum Learning on Recurrent Neural Networks
## 5 Discussion  
In this paper, we propose a method of applying curriculum learning to singing voice separation by adjusting the weight of the loss function. In order to apply curriculum learning, it is necessary to set the difficulty level of each data. We hypothesized that the model is easy to learn characteristics when one source component is dominant. The dominance of each source can be defined by the ratio of one source to the other, which can be obtained from the training data. Using this definition of source dominance, we can apply curriculum learning to singing voice separation by learning more of the different difficulty levels for each train stage. We conducted three experiments to verify the effectiveness of this method. GNSDR was significantly improved by at least 0.12 dB and up to 1.64 dB for two models and three data sets. These experimental results show that the proposed curriculum learning is effective in hard problems such as singing voice separation. Conceptualization, S.K. and [PERSON]-[PERSON]; methodology, S.K. ; software, S.K. ; validation, S.K. and [PERSON]-[PERSON]; formal analysis, S.K., [PERSON]-[PERSON] and [PERSON]-[PERSON]; investigation, [PERSON]-[PERSON] and [PERSON]-[PERSON]; resources, S.K. ; data curation, S.K. ; writing-original draft preparation, S.K. ; writing-review and editing, [PERSON]-[PERSON] and [PERSON]-[PERSON]; visualization, S.K. ; supervision, [PERSON] and [PERSON]-[PERSON]; project administration, [PERSON]; funding acquisition, [PERSON]. All authors have read and agree to the published version of the manuscript. This work was supported by Institute for Information and communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. R7124-16-0004, Development of Intelligent Interaction Technology Based on Context Awareness and Human Intention Understanding), the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2017M3C1B6071400), and the BK21 Plus project funded by the Ministry of Education, Korea (21A20131600011). The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l l l l l l} \hline \hline  & \multicolumn{4}{c}{**Instrument**} & \multicolumn{4}{c}{**Vocal**} \\ \hline  & **Model** & \(GSIR_{m}\) & \(GSAR_{m}\) & \(GNSDR_{m}\) & \(GSIR_{v}\) & \(GSAR_{v}\) & \(GNSDR_{v}\) \\ \hline \multirow{2}{*}{RNN} & baseline & 15.41 & 11.48 & 3.15 & 10.34 & 3.91 & 8.55 \\  & proposed & \(\dagger\)**17.10** & \(\dagger\)**12.26** & \(\dagger\)**4.21** & 10.56 & 4.27 & 8.98 \\ \hline \multirow{2}{*}{U-Net} & baseline & 13.42 & 15.06 & 4.10 & 10.97 & 3.96 & 8.84 \\  & proposed & \(\dagger\)**14.03** & \(\dagger\)**14.97** & 4.48 & \(\dagger\)**10.61** & \(\dagger\)**4.73** & \(\dagger\)**9.48** \\ \hline \hline \end{tabular}   
## 5 Discussion  
In this paper, we propose a method of applying curriculum learning to singing voice separation by adjusting the weight of the loss function. In order to apply curriculum learning, it is necessary to set the difficulty level of each data. We hypothesized that the model is easy to learn characteristics when one source component is dominant. The dominance of each source can be defined by the ratio of one source to the other, which can be obtained from the training data. Using this definition of source dominance, we can apply curriculum learning to singing voice separation by learning more of the different difficulty levels for each train stage. We conducted three experiments to verify the effectiveness of this method. GNSDR was significantly improved by at least 0.12 dB and up to 1.64 dB for two models and three data sets. These experimental results show that the proposed curriculum learning is effective in hard problems such as singing voice separation. Conceptualization, S.K. and [PERSON]; methodology, S.K. ; software, S.K. ; validation, S.K. and [PERSON]; formal analysis, S.K., [PERSON] and [PERSON]; investigation, [PERSON] and [PERSON]-[PERSON]; resources, S.K. ; data curation, S.K.