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Two dictionaries matching pursuit for sparse decomposition of signals

机译:两个字典匹配追求信号的稀疏分解

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Signal may be decomposed sparsely and power focally in an over-complete dictionary with matching pursuit (MP). In this paper, proposed is a modified NIP method named two dictionaries MP (TDMP) to decompose signal more sparsely. In the iteration procedure of TDMP, the over-complete dictionary is classified into two separate dictionaries with the selected and unselected atoms, and in each iteration, the algorithm was designed to have more chances than the original NIP to choose the atom in the selected atom dictionary as the optimal atom by the constraint of a simulate annealing threshold function, thus the algorithm avails for a more sparse decomposition. The decomposition results for a cosine-modulated exponential signal and an actual speech signal showed that the proposed TDMP could decompose signal more sparsely. (c) 2006 Elsevier B.V. All rights reserved.
机译:信号可能会在具有匹配追踪(MP)的过于完整的词典中稀疏地和集中地分解。本文提出了一种改进的NIP方法,称为两个字典MP(TDMP),以更稀疏地分解信号。在TDMP的迭代过程中,将完全完成的字典分为具有选择的原子和未选择的原子的两个单独的字典,并且在每次迭代中,该算法被设计为比原始NIP在选择的原子中具有更大的机会通过模拟退火阈值函数的约束将字典作为最佳原子,因此该算法可用于更稀疏的分解。余弦调制指数信号和实际语音信号的分解结果表明,提出的TDMP可以更稀疏地分解信号。 (c)2006 Elsevier B.V.保留所有权利。

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