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.

中文翻译：

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盲源分离的广义最小失真原理

我们从盲源分离（BSS）中重新审视源图像估计问题。我们使用残差谱图模型将传统的最小失真原理推广到最大似然估计。由于残差频谱图通常包含其他来源，我们建议使用混合范数模型，让我们可以微调时间和频率的稀疏性。我们建议通过主要化-最小化优化来执行混合范数的最小化，从而产生迭代重新加权的最小二乘算法。该算法很好地平衡了效率和易于实现。我们评估了所提出的方法在应用于两个众所周知的确定 BSS 和一个联合 BSS 去混响算法时的性能。我们发现可以调整参数以将分离度提高多达 2 dB，失真不会增加，并且计算成本很低。因此，该方法提供了一种廉价且简单的方法来提高盲源分离的性能。