Improved recognition performance for orthogonal sources

S. Grigis, A. Holobar, D. Zazula
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Abstract

This paper deals with the problem of recognition of multiple orthogonal sources buried in highly superimposed observations. The known blind source separation (BSS) approach is upgraded with a separation of sources using a classification procedure. Single source contributions are looked for in spatial time-frequency distribution (STFD) of the observed signals. The classification is based on STFD matrices which are grouped in the orthogonal and similar classes. The resulting separation algorithm outperforms other known approaches, as well in accuracy as by lower computational complexity.
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改进了正交源的识别性能
本文研究了埋藏在高度叠加观测数据中的多个正交源的识别问题。将已知的盲源分离(BSS)方法升级为使用分类过程的源分离。在观测信号的空间时频分布(STFD)中寻找单源贡献。该分类基于STFD矩阵,该矩阵被分组为正交类和相似类。所得到的分离算法优于其他已知的方法,并且在准确性和较低的计算复杂度方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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