## 3 Learned Separation
### Training
The authors would like to thank [PERSON] and [PERSON] for sharing their expertise and ideas on the learning approach. Further, we thank the anonymous reviewers, whose valuable and constructive comments helped us improve the manuscript. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} DIP & Deep image prior \\ GAN & Generative adversarial network \\ MCTS & Monte Carlo tree search \\ NMF & Non-negative matrix factorization \\ PLCA & Probabilistic latent component analysis \\ REINFORCE & Reward increment \(=\) Nonnegative factor \(\times\) Offset reinforcement \\  & \(\times\) Characteristic eligibility \\ SAR & Signal-to-artifacts ratio \\ SDR & Signal-to-distortion ratio \\ SIR & Signal-to-interference ratio \\ STFT & Short-time Fourier transform \\ URMP & University of Rochester Multi-Modal Music Performance Dataset \\ \end{tabular} 

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