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A Combined Approach using Subspace and Beamforming Methods for Time-Frequency Domain Blind Source Separation

机译:使用子空间和波束成形方法进行时间频域盲源分离的组合方法

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Blind source separation (BSS) is an approach to estimate original source signals only by measurements through multiple sensors without any prior knowledge about their mixture process. BSS is currently applied to a broad range of fields, such as pre-processing of speech recognition, analysis of bio-signals, etc. In BSS of convolutive mixtures, the signals must be expanded into time-frequency domains. In this case, permutation between frequencies becomes indefinite. This paper proposes a method for solving the indefiniteness problem by using directional peaks in addition to directional nulls in directivity patterns. This method makes it possible to combine the beamforming and subspace methods and to improve the accuracy in separatability of BSS. The effectiveness of the proposed method is demonstrated by computer simulation.
机译:盲源分离(BSS)是仅通过通过多个传感器的测量来估计原始源信号的方法,而无需任何关于其混合过程的先验知识。 BSS目前应用于广泛的字段,例如语音识别的预处理,对卷曲混合的BSS的生物信号等分析,必须将信号扩展到时频域中。在这种情况下,频率之间的置换变得无限期。本文提出了一种通过在方向性模式中的定向零点之外使用方向峰来解决无限峰的方法。该方法使得可以组合波束成形和子空间方法,并提高BSS间可分离性的准确性。通过计算机模拟证明了所提出的方法的有效性。

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