Generalized sparse signal mixing model and application to noisy blind source separation
Generalized sparse signal mixing model and application to noisy blind source separation
复制标题
广义稀疏信号混合模型及其在噪声盲源分离中的应用
DOI：
10.1109/icassp.2004.1326685
复制
发表时间：
2004
期刊：
影响因子：
--
通讯作者：
R. Balan
中科院分区：
文献类型：
--
作者：
Justinian P. Rosca;C. Borß;R. Balan
信号分解的稀疏约束可以通过声学、医学成像或无线等各种信号处理领域中使用的典型传感器数据来证明，而且可以产生更有效的算法。在这项工作中使用的特定稀疏性假设是，在任何时间和频率点的混合信号中统计独立的源活动的最大数量是小的。这是从源本身的稀疏性的假设，并允许我们解决的最大似然制定的非瞬时声混合源估计问题。考虑一个加性噪声混合模型，当源满足稀疏性假设时，具有任意数量的传感器和可能比传感器更多的源。所得到的解决方案是适用于任意数量的麦克风和源，但最好的工作时，在任何时间频率点同时活跃的源的数量是源的总数的一小部分。
Sparse constraints on signal decompositions are justified by typical sensor data used in a variety of signal processing fields such as acoustics, medical imaging, or wireless, but moreover can lead to more effective algorithms. The specific sparseness assumption used in this work is that the maximum number of statistically independent sources active at any time and frequency point in a mixture of signals is small. This is shown to result from an assumption of sparseness of the sources themselves, and allows us to solve the maximum likelihood formulation of the noninstantaneous acoustic mixing source estimation problem. We consider an additive noise mixing model with an arbitrary number of sensors and possibly more sources than sensors, when sources satisfy the sparseness assumption above. The solution obtained is applicable to an arbitrary number of microphones and sources, but works best when the number of sources simultaneously active at any time frequency point is a small fraction of the total number of sources.