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On Initial Seed Selection for Frequency Domain Blind Speech Separation

机译:频域盲语音分离的初始种子选择

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In this paper we address the problem of initial seed selection for frequency domain iterative blind speech separation (BSS) algorithms. The derivation of the seeding algorithm is guided by the goal to select samples which are likely to be caused by source activity and not by noise and at the same time originate from different sources. The proposed algorithm has moderate computational complexity and finds better seed values than alternative schemes, as is demonstrated by experiments on the database of the SiSEC2010 challenge.
机译:在本文中,我们解决了频域迭代盲语音分离(BSS)算法的初始种子选择问题。播种算法的推导以目标为指导,以选择可能是由源活动而不是由噪声引起并且同时源于不同源的样本。所提出的算法具有适度的计算复杂度,并且比替代方案具有更好的种子值,如通过对SiSEC2010挑战数据库进行的实验所证明的。

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