卷积混叠的盲信号分离是盲源分离问题中的难点。
The blind separation of convolution mixture signals is a nodus in blind source separation.
论文的主要研究对象是盲源分离问题。
The subject of this article is about the Blind Source Separation (BSS).
该文提出一种基于二阶统计量的时域多步分解算法求解卷积混合盲源分离问题。
A time-domain Multi-Stage Algorithm(MSA) based on the second order statistics for blind source separation of convolutive mixtures is proposed.
摘要：本文回顾了一些最新的求解非线性盲源分离问题的神经网络算法。
Abstract: in this paper, several recently proposed neural network approaches to nonlinear blind signal separation (BSS) are reviewed.
本文从信息论的角度入手，研究线性卷积混合的盲解卷积问题，成功实现了基于信息最大化准则的反馈网络算法对两源信号的分离。
From the perspective of information, the paper studies the linear blind deconvolution, successful separate two sources based on information maximization algorithm to the feedback network.
盲源分离问题的提出很好地解决了这一难题。
Blind source separation is put forward to solve the problem well.
将其用于盲源分离，通过实例证明了该方法的正确性和有效性，从而解决了盲分离中信号源个数的估计问题，为盲源分离技术的应用进一步奠定了基础。
The validity of the method in BSS is proved through experiments. Thus the method can solve the problem of estimation of signal number in BSS, it paves the way to wider application of BSS methods.
该文研究超定盲信号分离，即观测信号个数不少于源信号个数情况下的盲信号分离问题。
The problem of overdetermined Blind source Separation (BSS) where there are more mixtures than sources is considered.
对于盲源分离的两个固有不确定性问题，引入波形相似度的概念，使问题得到解决。
Introduce the concept of waveform similarity to deal with the two two inherent uncertainties of Blind Source Separation.
简要介绍了基于模拟退火思想的粒子群算法的基本原理，并将之应用于盲源分离算法中，以解决基本粒子群算法收敛速度缓慢的问题。
The basic principal of particle swarm optimization based on simulate anneal was introduced and was applied to blind source separation to solve the problem of low searching speed.
简要介绍了基于模拟退火思想的粒子群算法的基本原理，并将之应用于盲源分离算法中，以解决基本粒子群算法收敛速度缓慢的问题。
The basic principal of particle swarm optimization based on simulate anneal was introduced and was applied to blind source separation to solve the problem of low searching speed.
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