The improvement of the performance of online separating speech and music is an $NP$ problem and the separation optimization increases the complexity of the method in a Robust Principal Component Analysis (RPCA) method which is time consuming in big size matrix computations. This paper presents a RPCA-based speech and music separation method to reduce the amount of computational complexity and be robust to artificial noise by proposing two novel algorithms. The key idea of our real-time method is designing a novel random singular value decomposition algorithm in a non-convex optimization environment to significantly decrease the complexity of previous RPCA methods from $min(m{n}^2,m^{2}n)$ $flops$ to $mnr$ $flops$ where $r\ll min(m,n)$ to obtain better performance and get qualified results. Experimental results of different datasets compared with the best state-of-the-art method show that the proposed method is more reliable and achieves an average 339% speedup by the significant reduction of computational complexity, increases the quality of the speech signal by 295%, improves the quality of the music signal by 244% and the robustness of artificial noise without needing any learning technique or requiring particular features.

在线分离语音和音乐的性能的提高是 $ñP$问题和分离优化增加了鲁棒主成分分析（RPCA）方法的方法的复杂性，这在大尺寸矩阵计算中非常耗时。本文提出了一种基于RPCA的语音和音乐分离方法，该方法通过提出两种新颖的算法来降低计算复杂性并且对人造噪声具有鲁棒性。我们的实时方法的关键思想是在非凸优化环境中设计一种新颖的随机奇异值分解算法，以显着降低以前的RPCA方法的复杂性。 $米\mathrm{一世}ñ（米{ñ}^2，{米}^2ñ）$ $F升Øps$ 至 $米ñ\mathrm{[R}$ $F升Øps$ 哪里 $\mathrm{[R}\ll 米\mathrm{一世}ñ（米，ñ）$以获得更好的性能并获得合格的结果。与最佳技术水平相比，不同数据集的实验结果表明，该方法更加可靠，并且通过显着降低计算复杂度实现了平均339％的提速，使语音信号质量提高了295％无需任何学习技术或不需要特殊功能即可将音乐信号的质量提高244％，并提高了人工噪声的鲁棒性。