ed362831-c961-4df9-9c27-8b7fd3aec3e020210222045203350naunmdt@crossref.orgMDT DepositInternational Journal of Circuits, Systems and Signal Processing1998-446410.46300/9106http://www.naun.org/cms.action?id=3029118202111820211510.46300/9106.2021.15https://naun.org/cms.action?id=23283Music Signal Separation Using Supervised Robust Non-Negative Matrix Factorization with β-divergenceFengLiDepartment of Computer Science and Technology, Anhui University of Finance and Economics, Caoshan Road, Bengbu 233030, ChinaHaoChangDepartment of Computer Science and Technology, Anhui University of Finance and Economics, Caoshan Road, Bengbu 233030, ChinaWe propose a supervised method based on robust non-negative matrix factorization (RNMF) for music signal separation with β-divergence called supervised robust non-negative matrix factorization (SRNMF). Although RNMF method is an effective method for separating music signals, its separation performance degrades due to has no prior knowledge. To address this problem, in this paper, we develop SRNMF that unifying the robustness of RNMF and the prior knowledge to improve such separation performance on instrumental sound signals (e.g., piano, oboe and trombone). Application to the observed instrumental sound signals is an effective strategy by extracting the spectral bases of training sequences by using RNMF. In addition, β-divergence based on SRNMF be extended. The results obtained from our experiments on instrumental sound signals are promising for music signal separation. The proposed method achieves better separation performance than the conventional methods.22220212222021149154https://www.naun.org/main/NAUN/circuitssystemssignal/2021/a322005-016(2021).pdf10.46300/9106.2021.15.16https://www.naun.org/main/NAUN/circuitssystemssignal/2021/a322005-016(2021).pdfA. Mesaros, T. Virtanen, and A. Klapuri, “Singer identification in polyphonic music using vocal separation and pattern recognition methods,” in Proc. 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