RPCA-based real-time speech and music separation method

Abstract

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(mn2,m2n)flops to mnrflops where r≪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.