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LPCCandMFCC
自己做设计用的参数提取程序,主要是LPCC和MFCC。(Designs its own program with the parameter extraction, mainly LPCC and MFCC.)
- 2010-09-06 10:51:26下载
- 积分:1
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MATLAB_based_parameter_identification_and_simulati
基于MATLAB的递推最小二乘法辨识与仿真,很实用(MATLAB-based parameter identification and simulation, very useful)
- 2010-11-19 15:31:22下载
- 积分:1
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aa228
用于求一个信号的斜率,通过构造一个滤波器来实现的,绝对正确。(For seeking the slope of a signal, to be achieved by constructing a filter, absolutely correct.)
- 2012-03-29 10:11:41下载
- 积分:1
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solve-linear-equations-in-matlab
详细介绍了用迭代法求解线性方程组的方法及其Matlab语言表示。(Iterative method described in detail with the method for solving linear equations and Matlab language said.)
- 2011-05-14 12:46:03下载
- 积分:1
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write_CSV
用matlab把csv文件转换成码头文件,程序简单(Csv file into a terminal file using matlab, simple procedures)
- 2013-04-17 22:39:49下载
- 积分:1
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PCAFisher
主元分析,还不错,希望对大家有帮助
主元分析,还不错,希望对大家有帮助(Principal component analysis, but also good, and they hope to have everyone help PCA, but also good, and they hope to have everyone help)
- 2007-10-10 17:03:07下载
- 积分:1
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本程序用于仿真QPSK的解调-上交1
本程序用于仿真QPSK的解调(procedures for the simulation of the QPSK demodulator)
- 2005-03-13 22:42:01下载
- 积分:1
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Euler-method
高斯赛德尔消元法、Heun法等实现线性方程组的求解、另附一份MATLAB之GUI设计基础(High Sisaideer elimination method, Heun method to achieve linear equations, attach a MATLAB GUI design basis)
- 2013-03-29 23:22:28下载
- 积分:1
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chazhi
详细介绍了matlab插值与拟合方法,包括拉格朗日多项式插值、牛顿插值、分段线性插
值、Hermite 插值和三次样条插值和曲线的最小二乘拟合、多项式拟合方法、最小二乘优化所有程序均有相应的说明与应用实例(Details of the matlab interpolation and fitting methods, including Lagrange polynomial interpolation, Newton interpolation, piecewise linear interpolation
Value, Hermite interpolation and cubic spline interpolation and least-squares curve fitting, polynomial fitting method, least squares optimization program has all the appropriate instructions and application examples)
- 2013-12-08 20:21:36下载
- 积分:1
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RICE-UNIVERSITY
标准压缩感知(CS)理论决定了可靠的信号恢复是可能给M= O(KLOG(N / K))的测量。我们证明了它可以通过利用超越简单的稀疏性和可压缩性由包括价值观和信号系数的位置之间的依赖关系更加逼真信号模型大大降低Mwithout牺牲的鲁棒性。(The standard compressive sensing (CS) theory dictates that robust signal recovery is possible from M=O(Klog(N/K)) measurements. We demonstrate that it is possible to substantially decrease Mwithout sacrificing robustness by leveraging more realistic signal models that go beyond simple sparsity and compressibility by including dependencies between values and locations of the signal coefficients.
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- 2014-01-06 20:07:54下载
- 积分:1