论文标题
盲源分离的广义最小失真原理
Generalized Minimal Distortion Principle for Blind Source Separation
论文作者
论文摘要
我们从盲源分离（BSS）重新审视源图像估计问题。我们将传统的最小失真原理推广到最大似然估计，并使用残留光谱图的模型。由于残留频谱图通常包含其他来源，因此我们建议使用混合模型，该模型使我们可以在时间和频率上细微地调节稀疏性。我们建议通过大量最小化优化进行混合符号的最小化，从而导致迭代重新加权最小二乘算法。该算法平衡了良好的效率和易于实施。我们评估了所提出的方法的性能，该方法适用于两个众所周知的确定的BS和一种联合BSS-散布算法。我们发现，有可能调整参数以最多2 dB的速度改善分离，而不会增加失真，并且以几乎没有计算成本的价格提高了分离。因此，该方法为提高盲源分离的性能提供了一种便宜，简便的方法。
We revisit the source image estimation problem from blind source separation (BSS). We generalize the traditional minimum distortion principle to maximum likelihood estimation with a model for the residual spectrograms. Because residual spectrograms typically contain other sources, we propose to use a mixed-norm model that lets us finely tune sparsity in time and frequency. We propose to carry out the minimization of the mixed-norm via majorization-minimization optimization, leading to an iteratively reweighted least-squares algorithm. The algorithm balances well efficiency and ease of implementation. We assess the performance of the proposed method as applied to two well-known determined BSS and one joint BSS-dereverberation algorithms. We find out that it is possible to tune the parameters to improve separation by up to 2 dB, with no increase in distortion, and at little computational cost. The method thus provides a cheap and easy way to boost the performance of blind source separation.
