Speech signal sparse decomposition with FOA

Abstract

Abstract: Sparse representation of signals has many important applications in signal processing, but the computational burden in signal sparse decomposition process is so huge that it’s almost impossible to apply it to industrialization. In this paper, Fruit fly optimization algorithm is implemented to fast search for optimal atom at each step of Matching Pursuit（MP）, and the speed of signal sparse decomposition is improved. The validity of this algorithm is proved by experimental results.

Key words: speech signal, sparse decomposition, Matching Pursuit（MP）, Fruit Fly Optimization Algorithm（FOA）

摘要： 信号的稀疏表示在信号处理的许多方面有着重要的应用，但稀疏分解计算量十分巨大，难以产业化应用。利用果蝇优化算法实现快速寻找匹配追踪（MP）过程每一步的最优原子，大大提高了语音信号稀疏分解的速度，算法的有效性为实验结果所证实。

关键词: 语音信号, 稀疏分解, 匹配追踪, 果蝇优化算法

XIAO Zheng’an. Speech signal sparse decomposition with FOA[J]. Computer Engineering and Applications, 2013, 49(10): 232-234.

肖正安. 语音信号稀疏分解的FOA实现[J]. 计算机工程与应用, 2013, 49(10): 232-234.

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Speech signal sparse decomposition with FOA

语音信号稀疏分解的FOA实现

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