首页> 外文会议>Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE >Temporal alignment, spatial spread and the linear independence criterion for blind separation of voices
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Temporal alignment, spatial spread and the linear independence criterion for blind separation of voices

机译:时间对准,空间扩展和语音盲分离的线性独立性准则

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The usefulness of the neural network method of Matsuoka et al. [1995] for separating a mixture of two signals is investigated. The method appears to be very effective at separating signals which have been combined synthetically, but much less effective at separating a mixture of two real voices recorded with a pair of microphones. The algorithm was applied to specific examples to determine how critical it is that they be temporally aligned and that there be no spatial spread of the sources. The results indicate that the algorithm is very sensitive to temporal misalignment of voice mixture signals, whilst the spatial spread of the voice sources is less significant. This suggests that adaptive alignment of the mixture signals before signal separation may be beneficial.
机译:松冈等人的神经网络方法的有用性。 [1995]研究了分离两种信号的混合物。该方法似乎对分离已经合成的信号非常有效,但是对分离用一对麦克风录制的两个真实声音的混合效果不明显。该算法已应用于特定示例,以确定它们在时间上对齐以及源没有空间分布的重要性。结果表明,该算法对语音混合信号的时间未对准非常敏感,而语音源的空间扩展则不那么重要。这表明在信号分离之前混合信号的自适应对准可能是有益的。

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