张华伟,,
刘董华,
李群
重庆邮电大学 信号与信息处理重庆市重点实验室 ??重庆 ??400065
基金项目:国家自然科学基金(61671095, 61371164, 61702065, 61701067, 61771085)，信号与信息处理重庆市市级重点实验室建设项目(CSTC2009CA2003)，重庆市研究生科研创新项目(CYS17219)，重庆市教育委员会科研项目(KJ1600427, KJ1600429)
详细信息
作者简介:张天骐：男，1971年生，博士后，教授，主要研究方向为通信信号的调制解调、盲处理、语音信号处理、神经网络实现以及FPGA, VLSI实现
张华伟：女，1993年生，硕士生，研究方向为盲源分离算法改进
刘董华：男，1992年生，硕士生，研究方向为通信信号盲估计
李群：女，1991年生，硕士生，研究方向为直扩信号的盲处理
通讯作者:张华伟 1490714614@qq.com
中图分类号:TN911.7计量
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被引次数:0
出版历程
收稿日期:2018-04-25
修回日期:2018-10-10
网络出版日期:2018-10-24
刊出日期:2019-03-01
Frequency Domain Blind Source Separation Permutation Algorithm Based on Regional Growth Correction
Tianqi ZHANG,Huawei ZHANG,,
Donghua LIU,
Qun LI
Chongqing Key Laboratory of Signal and Information Processing, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Funds:The National Natural Science Foundation of China (61671095, 61371164, 61702065, 61701067, 61771085), The Project of Key Laboratory of Signal and Information Processing of Chongqing (CSTC2009CA2003), Chongqing Graduate Research and Innovation Project (CYS17219), The Research Project of Chongqing Educational Commission (KJ1600427, KJ1600429)
摘要
摘要:卷积盲源分离可以在频域得到有效解决，但频域盲源分离必须解决排序模糊问题。该文提出一种基于区域增长校正的频域盲源分离排序算法。首先对卷积混合信号短时傅里叶变换，在频域的各个频点处建立瞬时模型进行独立分量分析，在此基础上使用分离信号功率比的相关性，对所有频点进行逐点排序置换。其次根据阈值将排序后的结果划分为若干个小区域。最后按区域增长方式进行区域置换与合并，最终得到正确的分离信号。区域增长校正可最大限度地减少频点排序错误扩散现象，从而改善分离效果。在模拟和真实环境中分别进行语音盲源分离实验，结果表明所提算法的有效性。
关键词:卷积盲源分离/
频域排序/
区域增长/
功率比相关
Abstract:The convolutive blind source separation can be effectively solved in frequency domain, but blind source separation in frequency domain must solve the problem of ranking ambiguity. A frequency-domain blind source separation sorting algorithm is proposed based on regional growth correction. First, the convolutional mixed signal short-time Fourier transform is used to establish an instantaneous model at each frequency point in the frequency domain for independent component analysis. Based on this, the correlation of the power ratio of the separated signal is used to sort all frequency points one by one replacement. Second, according to the threshold, the sorted result is divided into several small areas. Finally. regional replacement and merging is performed according to the regional growth method, and the correct separation signal is finally obtained. Regional growth correction minimizes the mis-proliferation of frequency sorting and improves separation results. The speech blind source separation experiments are performed in the simulated and real environments respectively. The results show the effectiveness of the proposed algorithm.
Key words:Convolutive blind source separation/
Frequency domain permutation/
Region growing/
Power ratio correlation
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