论文标题
基于独立组件分析的盲目分离盲源分离的统一观点
A Unifying View on Blind Source Separation of Convolutive Mixtures based on Independent Component Analysis
论文作者
论文摘要
在许多日常生活的情况下，只能与其他干扰源一起观察到围栏中记录的声源。因此，备卷式的盲源分离（BSS）是音频信号处理中的核心问题。基于独立组件分析（ICA）的方法在该领域中尤为重要，因为它们仅需要很少的假设和弱的假设，并且可以对原始源信号和声学传播路径产生失明。当前使用的大多数算法都属于以下三个家族之一：频域ICA（FD-ICA），独立矢量分析（IVA）和用于备速混合物（Trinicon）的Triple-N独立组分分析。尽管ICA，FD-a和IVA之间的关系由于结构而变得显而易见，但与特立尼森的关系尚未确定。本文通过对这些算法及其差异的共同构件进行深入处理，从而填补了这一空白，从而为所有考虑的算法提供了一个共同的框架。
In many daily-life scenarios, acoustic sources recorded in an enclosure can only be observed with other interfering sources. Hence, convolutive Blind Source Separation (BSS) is a central problem in audio signal processing. Methods based on Independent Component Analysis (ICA) are especially important in this field as they require only few and weak assumptions and allow for blindness regarding the original source signals and the acoustic propagation path. Most of the currently used algorithms belong to one of the following three families: Frequency Domain ICA (FD-ICA), Independent Vector Analysis (IVA), and TRIple-N Independent component analysis for CONvolutive mixtures (TRINICON). While the relation between ICA, FD-ICA and IVA becomes apparent due to their construction, the relation to TRINICON is not well established yet. This paper fills this gap by providing an in-depth treatment of the common building blocks of these algorithms and their differences, and thus provides a common framework for all considered algorithms.
