[1]郭海亮. 基于GEP算法的压缩感知语音观测序列建模[J].计算机技术与发展,2015,25(05):46-51.
　GUO Hai-liang. Speech Signals Measurements Sequence Modeling in Compressed Sensing Based on GEP[J].,2015,25(05):46-51.
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 基于GEP算法的压缩感知语音观测序列建模()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年05期
页码:
46-51
栏目:
智能、算法、系统工程
出版日期:
2015-05-10

文章信息/Info

Title:
 Speech Signals Measurements Sequence Modeling in Compressed Sensing Based on GEP
文章编号:
1673-629X（2015)05-0046-06
作者:
 郭海亮
 陕西师范大学 计算机科学学院
Author(s):
 GUO Hai-liang
关键词:
 ; 压缩感知; 语音观测序列; 基因表达式编程; 二次压缩
Keywords:
 ; CS; speech measurements sequence; GEP; secondary compression
分类号:
TP391
文献标志码:
A
摘要:
 为了进一步减少压缩感知中语音信号观测序列的数据传输量,文中采用基因表达式编程( Gene Expression Pro-gramming,GEP)算法对语音信号的观测序列进行建模与预测,同时引入观测序列建模预测后的压缩感知理论框架来解决该问题。首先,分析了压缩感知中语音信号观测序列的相关特性,然后利用GEP算法对语音信号观测序列建立了精确的非线性模型结构,最终实现原始语音信号的重构。实验结果表明,该算法在保证重构语音的性能的前提下,可以进一步减少语音信号观测序列的传输量,最终实现语音信号的二次压缩。
Abstract:
 In order to reduce the amount of data transmission of speech signals measurements sequence in compressed sensing,use GEP al-gorithm to model and predict for the measurements sequence of speech signal,and a CS theoretical framework is proposed after predicting and modeling the measurements sequence to fix this question. First,analyze the relevant characteristics of the measurements sequence of speech signal in compressed sensing. Then using GEP algorithm,build an accurate nonlinearity model structure for speech signals meas-urements sequence. Finally,achieve the goal of rebuilding original signals. Showed by experiment,this algorithm can further reduce the a-mount of measurements sequence,while ensuring the performance of reconstructed speech signals,to achieve the purpose of secondary compression of speech signals.

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更新日期/Last Update: 2015-06-23