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- W3113032861 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 mnr flops 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." @default.
- W3113032861 created "2020-12-21" @default.
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- W3113032861 date "2021-02-01" @default.
- W3113032861 modified "2023-10-16" @default.
- W3113032861 title "RPCA-based real-time speech and music separation method" @default.
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- W3113032861 doi "https://doi.org/10.1016/j.specom.2020.12.003" @default.
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