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首页> 外文期刊>EURASIP journal on advances in signal processing >Frequency-Domain Blind Source Separation of Many Speech Signals Using Near-Field and Far-Field Models
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Frequency-Domain Blind Source Separation of Many Speech Signals Using Near-Field and Far-Field Models

机译:使用近场和远场模型的许多语音信号的频域盲源分离

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摘要

We discuss the frequency-domain blind source separation (BSS) of convolutive mixtures when the number of source signals is large, and the potential source locations are omnidirectional. The most critical problem related to the frequency-domain BSS is the permutation problem, and geometric information is helpful as regards solving it. In this paper, we propose a method for obtaining proper geometric information with which to solve the permutation problem when the number of source signals is large and some of the signals come from the same or a similar direction. First, we describe a method for estimating the absolute DOA by using relative DOAs obtained by the solution provided by independent component analysis (ICA) and the far-field model. Next, we propose a method for estimating the spheres on which source signals exist by using ICA solution and the near-field model. We also address another problem with regard to frequency-domain BSS that arises from the circularity of discrete-frequency representation. We discuss the characteristics of the problem and present a solution for solving it. Experimental results using eight microphones in a room show that the proposed method can separate a mixture of six speech signals arriving from various directions, even when two of them come from the same direction.
机译:当源信号的数量大且潜在源位置是全向的时,我们讨论了卷积混合物的频域盲源分离(BSS)。与频域BSS相关的最关键的问题是置换问题,几何信息对于解决它是有帮助的。在本文中,我们提出了一种获取适当几何信息的方法,通过该方法可以解决当源信号数量大且某些信号来自相同或相似方向时的置换问题。首先,我们描述一种通过使用相对DOA估算绝对DOA的方法,该相对DOA是通过独立分量分析(ICA)和远场模型提供的解决方案获得的。接下来,我们提出一种使用ICA解和近场模型估算源信号所在球体的方法。我们还解决了有关频域BSS的另一个问题,该问题是由离散频率表示的圆度引起的。我们讨论了问题的特征并提出了解决方案。在一个房间中使用八个麦克风的实验结果表明,即使其中两个来自同一方向,该方法也可以分离出来自不同方向的六个语音信号的混合。

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