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Decompositional independent component analysis using multi-objective optimization

机译:使用多目标优化的分解独立成分分析

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摘要

Current approaches for blind source separation, such as independent component analysis (ICA), implicitly assume that the number of collected signals equals the number of sources. This assumption does not hold true in many real-world applications as in the case of electroencephalographic (EEG) data collected from the surface of a human's scalp, where independent EEG information is mixed with independent artifacts. This situation is abstracted in this paper by introducing the singers' party problem, where the number of signals collected from the party equals the number of singers. However, there are also a number of instruments playing at the party representing independent sources that need to be removed correctly to extract the voices of the singers. In this paper, we introduce a decompositional approach to project the sources found in ICA into a higher-dimensional space; providing the ability to separate local (singers) information from shared/global (instruments) information. The decomposition will also associate each component with a mixed signal, creating a bijective relationship between the mixed signals and the sources. The problem is formulated as a multi-objective optimization problem. We compare the pros and cons of two different multi-objective formulations of the problem and demonstrate that one of the formulations can effectively solve the singers party problem.
机译:当前用于盲源分离的方法,例如独立成分分析(ICA),隐含地假设所收集信号的数量等于源的数量。这种假设在许多实际应用中并不成立,就像从人类头皮表面收集的脑电图(EEG)数据一样,其中独立的EEG信息与独立的伪像混合在一起。本文通过介绍歌手的聚会问题来抽象这种情况,其中从聚会中收集的信号数量等于歌手的数量。但是,在聚会上也有许多乐器在演奏,它们代表着独立的音源,需要正确删除这些声音才能提取歌手的声音。在本文中,我们介绍了一种分解方法将ICA中发现的源投影到更高维度的空间中。提供将本地(歌手)信息与共享/全局(乐器)信息分开的功能。分解还将使每个分量与混合信号相关联,从而在混合信号和源之间创建双射关系。该问题被表述为多目标优化问题。我们比较了该问题的两种不同的多目标公式的优缺点,并证明了其中一种公式可以有效地解决歌手派对的问题。

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