Sparse component analysis techniques have been successfully applied to the separation of speech sources. This paper presents an efficient algorithm based on the matching pursuit approach to deal with multichannel records. The proposed algorithm explicitly employs spatial constraints among different channels to express mixed signals as linear combinations of delayed components selected from an overcomplete dictionary. We present a new procedure for estimating the mixing system parameters (attenuations and delays), which can be applied to more than two mixtures and is not restricted to non-negative attenuation coefficients. The proposed mixing system estimation method can accommodate delays of greater magnitude than traditional approaches. In addition, learned dictionaries that improve the identification step can be used when excerpts from sources (exogenous to mixtures) are available. The simulation results show that semi-blind dictionaries perform better than those used in blind configurations.

稀疏成分分析技术已成功应用于语音源的分离。本文提出了一种基于匹配追踪方法的有效算法来处理多通道记录。所提出的算法明确地利用不同通道之间的空间约束来将混合信号表示为从过完整字典中选择的延迟分量的线性组合。我们提出了一种估计混合系统参数（衰减和延迟）的新方法，该方法可以应用于两种以上的混合物，并且不限于非负衰减系数。所提出的混合系统估计方法可以适应比传统方法更大的延迟。此外，当有来自来源（混合物以外的来源）的摘录可用时，可以使用可改进识别步骤的学习词典。仿真结果表明，半盲字典的性能优于盲配置。