BLIND SOURCE SEPARATION WITH NON-STATIONARY MIXING USING WAVELETS
BLIND SOURCE SEPARATION WITH NON-STATIONARY MIXING USING WAVELETS
复制标题
使用小波进行非平稳混合的盲源分离
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
W. Addison
中科院分区：
文献类型：
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作者：
W. Addison
本文研究了混合过程是动态的情况下的盲源分离问题。首先，我们提出了一个新的伊卡算法的静态混合问题，利用小波表示的信号。在我们的实验中，这优于标准伊卡，从而允许从较少数量的样本中估计解混。我们用它来创建一个基于滑动窗口的算法，该算法能够跟踪盲源分离问题中的动态非平稳混合过程。每个新窗口的有效初始化基于在先前窗口中学习的解混过程的平滑估计来计算。这减少了更新每个窗口所需的计算，并减少了算法落入成本函数的不期望的局部最小值的机会。该算法的有效性证明了一些模拟数据使用人工混合音频源。
This paper addresses the problem of blind source separation in the situation where the mixing process is dynamic. We first present a new ICA algorithm for the static mixing problem that exploits a wavelet representation of the signals. This outperforms standard ICA in our experiments thus allowing the unmixing to be estimated from a smaller number of samples. We use this to create a sliding window based algorithm that is capable of tracking the dynamics a non-stationary mixing process in the blind source separation problem. An effective initialization for each new window is calculated based on a smoothed estimate of the unmixing process learnt in previous windows. This reduces the computation required for updating each window and reduces the chance of the algorithm falling into undesirable local minima of the cost function. The efficacy of the algorithm is demonstrated on some simulated data using artificially mixed audio sources.