基于样条插值与人工蜂群优化的非线性盲源分离算法

doi:10.11959/j.issn.1000-436x.2017147

Abstract

Abstract:

A post-nonlinear blind source separation algorithm based on spline interpolation fitting and artificial bee colony optimization was proposed for the more complicated nonlinear mixture situations.The separation model was constructed by using the spline interpolation to fit the inverse nonlinear distortion function and using entropy as the separation criterion.The spline interpolation node parameters were solved by the modified artificial bee colony optimization algorithm.The correlation constraint was added into the objective function for limiting the solution space and the outliers wuld be restricted in the separation process.The results of speech sounds separation experiment show that the proposed algorithm can effectively realize the signal separation for the nonlinear mixture.Compared with the traditional separation algorithm based on odd polynomial fitting,the proposed algorithm has higher separation accuracy.

Key words: blind source separation, post nonlinear, spline interpolation, swarm intelligence optimization, artificial bee colony algorithm

CLC Number:

TN911.7

Lei CHEN,Shi-zhong GAN,Li-yi ZHANG,Guang-yan WANG. Nonlinear blind source separation algorithm based on spline interpolation and artificial bee colony optimization[J]. Journal on Communications, 2017, 38(7): 36-46.

Figures/Tables 13

References 33

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性能指标	y_i		非线性畸变较弱情况			非线性畸变较强情况
性能指标	y_i	信号1	信号2	信号3	信号1	信号2	信号3
	y₁	0.962 8	0.753 7	0.855 3	0.857 7	0.579 4	0.485 8
AVCC	y₂	0.919 4	0.992 1	0.972 9	0.847 9	0.915 7	0.727 3
	y₃	0.824 2	0.785 2	0.989 0	0.778 4	0.759 1	0.594 9
	均值	0.902 1	0.843 7	0.939 1	0.828 0	0.751 4	0.602 7
	y₁	33.608 8	98.262 8	39.250 2	14.090 9	59.797 7	39.337 4
MSE(×10^-3)	y₂	6.217 9	0.799 2	16.613 2	32.167 8	6.012 8	35.192 7
	y₃	50.984 4	39.436 5	3.328 8	26.718 9	69.537 0	103.053 0
	均值	30.270 4	46.166 2	19.730 7	24.325 9	45.115 8	59.194 4
	y₁	2.528 0	3.727 2	3.666 3	6.292 2	-0.695 6	4.029 1
RSNR/dB	y₂	6.945 2	16.508 6	6.120 0	-0.192 9	7.472 5	2.855 9
	y₃	-1.238 0	5.140 2	14.831 7	1.568 1	1.253 9	0.118 0
	均值	2.745 1	8.458 7	8.206 0	2.555 8	2.676 9	2.334 3

性能指标	y_i		信号1			信号2			信号3
性能指标	y_i	文献[23]算法	文献[24]算法	本文算法	文献[23]算法	文献[24]算法	本文算法	文献[23]算法	文献[24]算法	本文算法
	y₁	0.996 1	0.997 9	0.998 3	0.998 6	0.999 3	0.998 0	0.998 9	0.996 1	0.997 0
AVCC	y₂	0.999 5	0.999 0	0.999 4	0.998 1	0.999 6	0.999 2	0.999 8	0.995 3	0.998 3
	y₃	0.994 3	0.999 2	0.998 8	0.999 2	0.998 8	0.999 5	0.998 2	0.994 5	0.999 4
	均值	0.996 6	0.998 7	0.998 8	0.998 6	0.999 2	0.998 9	0.999 0	0.995 3	0.998 2
	y₁	0.118 6	0.355 3	0.118 1	0.676 1	2.742 8	0.420 0	2.385 0	0.073 8	0.253 5
MSE(×10^-3)	y₂	0.034 2	0.180 2	0.029 0	0.427 2	0.140 4	0.087 2	0.049 9	0.144 8	0.260 5
	y₃	0.163 3	0.115 0	0.199 0	0.370 7	1.005 3	1.024 6	2.779 1	0.130 6	0.049 3
	均值	0.105 4	0.216 8	0.115 4	0.491 3	1.296 2	0.510 6	1.738 0	0.116 4	0.187 8
	y₁	21.567 8	15.222 7	23.371 9	25.335 0	28.387 3	23.030 6	27.059 8	20.582 4	22.275 6
RSNR/dB	y₂	32.812 9	26.846 4	28.915 6	22.655 3	23.653 2	24.104 1	30.263 2	20.614 9	22.322 9
	y₃	19.221 2	19.957 4	19.874 8	26.965 0	28.515 1	33.748 7	22.848 2	21.460 5	25.229 7
	均值	24.534 0	20.675 5	24.054 1	24.985 1	26.851 9	26.961 1	26.723 7	20.885 9	23.276 1

性能指标	y_i		信号1			信号2			信号3
性能指标	y_i	文献[23]算法	文献[24]算法	本文算法	文献[23]算法	文献[24]算法	本文算法	文献[23]算法	文献[24]算法	本文算法
	y₁	0.411 8	0.991 0	0.996 7	0.382 6	0.982 1	0.995 8	0.315 9	0.982 8	0.991 8
AVCC	y₂	0.952 5	0.992 1	0.996 8	0.842 4	0.991 7	0.996 4	0.718 0	0.997 1	0.998 6
	y₃	0.837 9	0.996 8	0.996 2	0.954 2	0.997 9	0.990 3	0.780 4	0.995 7	0.998 0
	均值	0.734 1	0.993 3	0.996 6	0.726 4	0.990 6	0.994 2	0.604 8	0.991 9	0.996 1
	y₁	75.308 0	1.236 6	0.401 9	80.494 9	3.830 6	0.354 7	165.088 6	3.929 3	3.107 5
MSE(×10^-3)	y₂	10.978 5	3.250 4	0.637 9	22.323 7	1.136 3	0.687 2	71.341 1	7.248 6	1.240 6
	y₃	20.888 5	1.920 7	0.671 1	52.725 2	2.255 1	2.070 1	119.473 7	4.537 4	1.452 5
	均值	35.725 0	2.135 9	0.570 3	51.847 9	2.407 3	1.037 3	118.634 5	5.238 4	1.933 5
	y₁	-0.986 8	21.071 9	24.746 3	-1.987 6	13.120 7	24.540 0	-2.572 5	14.098 0	15.297 4
RSNR/dB	y₂	4.475 9	15.874 9	21.312 7	1.774 2	16.951 7	20.389 6	-0.213 0	12.697 4	23.152 8
	y₃	2.637 3	22.228 5	21.877 3	2.454 5	20.951 5	17.846 7	-0.792 4	15.071 8	18.175 6
	均值	2.042 1	19.725 1	22.645 4	0.747 0	17.008 0	20.925 4	-1.192 6	13.955 7	18.875 3

性能指标	y_i		信号1			信号2			信号3
性能指标	y_i	文献[23]算法	文献[24]算法	本文算法	文献[23]算法	文献[24]算法	本文算法	文献[23]算法	文献[24]算法	本文算法
	y₁	0.375 9	0.769 4	0.989 0	0.343 9	0.875 8	0.989 3	0.259 6	0.836 3	0.941 6
AVCC	y₂	0.920 0	0.912 0	0.991 8	0.898 7	0.927 1	0.982 5	0.832 6	0.959 1	0.987 5
	y₃	0.855 7	0.877 6	0.991 4	0.933 5	0.961 5	0.978 7	0.811 9	0.973 1	0.978 0
	均值	0.717 2	0.853 0	0.990 7	0.725 4	0.921 5	0.983 5	0.634 7	0.922 8	0.969 0
	y₁	106.752 4	28.858 0	1.441 9	110.929 6	15.997 7	1.198 2	204.193 5	97.798 2	18.350 2
MSE(×10^-3)	y₂	27.951 7	15.063 3	3.504 7	34.870 4	7.371 1	2.896 0	49.049 0	22.938 0	4.836 9
	y₃	34.214 6	46.920 1	1.323 0	62.250 2	8.458 1	3.491 6	61.544 5	11.916 4	12.571 0
	均值	56.306 2	30.280 5	2.089 9	69.350 1	10.609 0	2.528 6	104.929 0	44.217 5	11.919 4
	y₁	-2.502 2	9.062 5	17.471 7	-3.380 4	6.763 3	18.535 1	-3.495 7	3.109 9	11.159 2
RSNR/dB	y₂	0.429 5	10.548 4	13.306 8	-0.162 7	8.155 4	13.984 1	1.414 1	9.502 1	16.741 4
	y₃	0.472 2	9.873 7	17.554 6	1.733 6	15.748 6	15.978 3	2.088 4	10.772 7	14.604 6
	均值	-0.533 5	9.828 2	16.111 0	-0.603 2	10.222 4	16.165 8	0.002 3	7.794 9	14.168 4

Nonlinear blind source separation algorithm based on spline interpolation and artificial bee colony optimization

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