Description: 文章提出了基于多带语减法改进的增强系统，并在TI会司的浮点处理器TMS320C6713上实现。改进的多带语减法将信号频语非线性的分成4个互不重叠的频带， 每个频带分别做不同系数的语减运算， 因此能很好的滤除有色噪声。工程实现证明此设计能有效的抑制色噪声和音乐噪声， 即便在低信嗓比环境也能有较好增强效果.-In this paper, a language based on multi-band subtraction improved augmentation system, and TI will Division TMS320C6713 floating-point processor to achieve. Improved multi-band frequency signal subtraction language phrase is divided into four non-linear non-overlapping frequency bands, each band separately by different coefficient computing language, so they can filter out a good colored noise. Proves that the design works to achieve effective inhibition of color noise and music noise, even in low sNR environment can also enhance the effect of better. Platform: |
Size: 367616 |
Author:凝雪 |
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Description: ex6_1 ～ ex6_3二项分布的随机数据的产生
ex6_4 ～ ex6_6通用函数计算概率密度函数值
ex6_7 ～ ex6_20常见分布的密度函数
ex6_21 ～ ex6_33随机变量的数字特征
ex6_34 采用periodogram函数来计算功率谱
ex6_35 利用FFT直接法计算上面噪声信号的功率谱
ex6_36 利用间接法重新计算上例中噪声信号的功率谱
ex6_37 采用tfe函数来进行系统的辨识，并与理想结果进行比较
ex6_38 在置信度为0.95的区间上估计有色噪声x的PSD
ex6_39 在置信度为0.95的区间上估计两个有色噪声x，y之间的CSD
ex6_40 用程序代码来实现Welch方法的功率谱估计
ex6_41 用Welch方法进行PSD估计，并比较当采用不同窗函数时的结果
ex6_42 用Yule-Walker AR法进行PSD估计
ex6_43 用Burg算法计算AR模型的参数
ex6_44 用Burg法PSD估计
ex6_45 比较协方差方法与改进的协方差方法在功率谱估计中的效果
ex6_46 用Multitaper法进行PSD估计
ex6_47 用MUSIC法进行PSD估计
ex6_48 用特征向量法进行PSD估计-ex6_1 ~ ex6_3 binomial distribution of the generated random data
ex6_4 ~ ex6_6 generic function value of the probability density function
ex6_7 ~ ex6_20 common distribution density function
ex6_21 ~ ex6_33 figure characteristics of random variables
periodogram function ex6_34 used to calculate the power spectrum
ex6_35 direct method using FFT signal above the noise of the power spectrum
ex6_36 recalculated using the indirect method on the example of the power spectrum of noise signal
tfe function ex6_37 used for system identification, and results were compared with the ideal
ex6_38 at 0.95 confidence interval for the estimated colored noise x on the PSD
ex6_39 at 0.95 confidence interval of the two colored noise on the estimated x, y between the CSD
ex6_40 code used to achieve the Welch method of power spectrum estimation
Welch method ex6_41 with PSD estimates, and compare different window function when the results when
ex6_42 using Yule-Walker AR method is esti Platform: |
Size: 7168 |
Author:张满超 |
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Description: ex6_1 ～ ex6_3二项分布的随机数据的产生
ex6_4 ～ ex6_6通用函数计算概率密度函数值
ex6_7 ～ ex6_20常见分布的密度函数
ex6_21 ～ ex6_33随机变量的数字特征
ex6_34 采用periodogram函数来计算功率谱
ex6_35 利用FFT直接法计算上面噪声信号的功率谱
ex6_36 利用间接法重新计算上例中噪声信号的功率谱
ex6_37 采用tfe函数来进行系统的辨识，并与理想结果进行比较
ex6_38 在置信度为0.95的区间上估计有色噪声x的PSD
ex6_39 在置信度为0.95的区间上估计两个有色噪声x，y之间的CSD
ex6_40 用程序代码来实现Welch方法的功率谱估计
ex6_41 用Welch方法进行PSD估计，并比较当采用不同窗函数时的结果
ex6_42 用Yule-Walker AR法进行PSD估计
ex6_43 用Burg算法计算AR模型的参数
ex6_44 用Burg法PSD估计
ex6_45 比较协方差方法与改进的协方差方法在功率谱估计中的效果
ex6_46 用Multitaper法进行PSD估计
ex6_47 用MUSIC法进行PSD估计
ex6_48 用特征向量法进行PSD估计
-ex6_1 ~ ex6_3 binomial random data
ex6_4 ~ ex6_6 generic function to calculate the probability density function value
ex6_7 ~ common distribution density function ex6_20
ex6_21 ~ ex6_33 numerical characteristics of random variables
ex6_34 using periodogram function to calculate the power spectrum
ex6_35 direct method using FFT power spectrum of the signal above the noise
ex6_36 use the indirect method to recalculate the case of the power spectrum of the noise signal
ex6_37 tfe functions to use the identification system and the results are compared with the ideal
ex6_38 the confidence level of the estimated colored noise x 0.95 range on PSD
ex6_39 the confidence level of the estimated two colored noise on the 0.95 x interval between the CSD y
ex6_40 application code to implement the Welch method of power spectrum estimation
The results ex6_41 with Welch PSD estimation method and compared using different window function when the time
ex6_42 with Yule-Walker AR method PSD esti Platform: |
Size: 5120 |
Author:Bruce |
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Description: 针对这些问题,在传统的四阶累积量多重信号分类（multiple signal classification,MUSIC）算法基础上提出了一种改进的算法。该算法根据四阶累积量矩阵构成的规律,去除了原四阶累积量矩阵的数据冗余, 有效地减小了运算时间,为其实际应用提供了必要条件。计算机仿真和实测数据仿真的结果表明,本改进算法可以快速地实现虚拟阵列扩展,同时对有色高斯噪声也 有一定的抑制-Aiming at these problems, an improved algorithm based on signal classification (MUSIC) algorithm is proposed, which is based on the traditional four order multiple. According to the rule of the four order accumulation matrix, the algorithm removes the data redundancy of the original four order cumulative quantity matrix, effectively reduces the computation time, and provides the necessary conditions for its practical application. The simulation results show that the improved algorithm can quickly realize the virtual array expansion, and it can also restrain the colored Gauss noise. Platform: |
Size: 1024 |
Author:maomaoyu |
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Description: This code is implemented for finding the DOD and DOA of a target with unknown colored noise. It uses Beamspace MUSIC for angle estimation Platform: |
Size: 4348 |
Author:evandovich |
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Search - music colored noise