Lee Seonjoo, Caffo Brian S, Lakshmanan Balaji, Pham Dzung L
Department of Psychiatry and Biostatistics, Columbia University.
Department of Biostatistics, Johns Hopkins University.
J Stat Comput Simul. 2015 Apr;85(6):1151-1164. doi: 10.1080/00949655.2013.867961.
Independent component analysis (ICA) is a popular blind source separation technique used in many scientific disciplines. Current ICA approaches have focused on developing efficient algorithms under specific ICA models, such as instantaneous or convolutive mixing conditions, intrinsically assuming temporal independence or autocorrelation of the sources. In practice, the true model is not known and different ICA algorithms can produce very different results. Although it is critical to choose an ICA model, there has not been enough research done on evaluating mixing models and assumptions, and how the associated algorithms may perform under different scenarios. In this paper, we investigate the performance of multiple ICA algorithms under various mixing conditions. We also propose a convolutive ICA algorithm for echoic mixing cases. Our simulation studies show that the performance of ICA algorithms is highly dependent on mixing conditions and temporal independence of the sources. Most instantaneous ICA algorithms fail to separate autocorrelated sources, while convolutive ICA algorithms depend highly on the model specification and approximation accuracy of unmixing filters.
独立成分分析（ICA）是一种在许多科学学科中广泛应用的盲源分离技术。当前的ICA方法主要集中在特定ICA模型下开发高效算法，如瞬时或卷积混合条件，本质上假设源信号的时间独立性或自相关性。在实际应用中，真实模型往往未知，不同的ICA算法可能会产生非常不同的结果。尽管选择ICA模型至关重要，但在评估混合模型和假设以及相关算法在不同场景下的性能方面，尚未进行足够的研究。在本文中，我们研究了多种ICA算法在各种混合条件下的性能。我们还针对回声混合情况提出了一种卷积ICA算法。我们的仿真研究表明，ICA算法的性能高度依赖于混合条件和源信号的时间独立性。大多数瞬时ICA算法无法分离自相关源信号，而卷积ICA算法则高度依赖于解混滤波器的模型规范和近似精度。
