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traditionalsp
- 语音信号的频域处理,语音虽然是一个时变、非平稳的随机过程。但在短时间内可近似看作是平稳的。因此如果能从带噪语音的短时谱中估计出“纯净”语音的短时谱，即可达到语音增强的目的。由于噪声也是随机过程，因此这种估计只能建立在统计模型基础上。利用人耳感知对语音频谱分量的相位不敏感的特性，这类语音增强算法主要针对短时谱的幅度估计。 -voice signals in the frequency domain processing, voice is a time-varying, nonstationa
tfarma10
- 用于模拟时变非平稳的ARMA过程，根据Doppler频移和时变参数计算ARMA过程的系数，可以用来模拟非平稳的多径衰落信道-used to simulate nonstationary time-varying ARMA process, according to Doppler frequency shift and the time-varying parameters ARMA process coefficient, can be used to simulate the non-sta
hybridSIREKF
- To estimate the input-output mapping with inputs x ％ and outputs y generated by the following nonlinear, ％ nonstationary state space model: ％ x(t+1) = 0.5x(t) + [25x(t)]/[(1+x(t))^(2)] ％ + 8cos(1.2t) + process noise ％ y(t) = x(t)^(2) / 2
HHT
- 台湾国立中央大学开发的EMD-HHT算法,其中,EMD-HHT的创始人为该中心的主任.-EMD-HHT IS SPECIALLY DESIGNED FOR PROCESSING NONSTATIONARY AND NONLINEAR SIGNALS. IT CAN DECOMPSE SIGNALS AND THEN RECONSTRUCT SIGNALS ACCORDING TO SOME CRITERIA. AFTER THE PROCESS, SIGNALS WOULD HAVE HIGH
NonstationaryChannelEstimation
- Nonstationary Channel Estimation using a Kalman Tracking Filter 卡尔曼滤波算法的一个一个用，可用作 efk学习之用-Nonstationary Channel Estimation using a Kalman Tracking Filter Kalman filter algorithm one by one, and can be used as a learning efk
herbordt2003
- Herbordt, W. Nakamura, S. & llermann, W. Multichannel estimation of the power spectral density of noise for mixtures of nonstationary signals IPSJ SIG Technical Reports, 2004 ,131 ,211 - 216-Herbordt, W. Nakamura, S. & llermann, W. Multichannel esti
noisetracking
- 包含M文件，培训和跟踪落实的噪音中描述的算法： [1] J.S.厄克伦斯和R. Heusdens，“非平稳噪声跟踪基于数据驱动的递归噪声功率的估计”，IEEE期刊。音频，语音卷。 16，第6页。1112年至1123年，2008年8月。 见Descr iption.doc在zip文件。-Contains m-files to train and implement the noise tracking algorithm described in:
Noise_Tracking
- 根据” J.S. Erkelens and R. Heusdens, "Tracking of nonstationary noise based on data-driven recursive noise power estimation”所开发的源码-noisetracker based on data-driven recursive noise power estimation
analyze-nonstationary-time-
- 分析非平稳时变的实证分解法（EMD）的基础上产生的适应性，本征模函数（IMF）的数据，处理的数据。-analyze the non-stationary time-varying data processed by the Empirical Decomposition Method (EMD), which generates the adaptive basis, Intrinsic Mode Functions (IMF), from the data. Each chapter pa
jdpgj
- 噪声中非平稳信号的频谱分析，基于MATLAB，实验报告一份-Nonstationary noise frequency spectrum analysis, based on MATLAB, a lab report
fpwxh
- 噪声中非平稳信号的频谱分析，基于MATLAB，实验报告一份-Nonstationary noise frequency spectrum analysis, based on MATLAB, a lab report
shipingshangshiyang1
- 求信号的时频熵，对非平稳信号进行emd分解后，求时频熵，得出其故障特征-For the signal s time-frequency entropy, the nonstationary signals are EMD decomposition, and time-frequency entropy, the fault feature
Hilbert
- 对于非平稳振动信号 ，通过采用hilbert变换，得到其边际谱的labview 程序-For the nonstationary vibration signals, by the use of the Hilbert transformation, get the marginal spectrum of labview program
10.1.1.11.5905
- This paper compares performance of nite impulse response (FIR) adaptive linear equalizers based on the recursive least-squares (RLS) and least mean square(LMS) algorithms in nonstationary uncorrelated scattering wireless channels. Simulation resul
010
- 小波分析在交通领域的应用（汽车变速器轴承故障诊断、变速器齿轮故障诊断和车辆非平稳振动分析）-Wavelet analysis in the application of the traffic (auto transmission bearing fault diagnosis, transmission gear fault diagnosis and vehicle the nonstationary vibration analysis)
wavelet
- 小波分析因其在处理非平稳信号中的独特优势而成为信号处理中的一个重要研究方向。如今随着小波理论体系的不断完善，小波以其时频局部化特性与多尺度特性在图像边缘检测领域中倍受青睐。-Wavelet analysis of nonstationary signal processing because of its unique advantage in signal processing and become an important research direction in. Now with th
Nonstationary
- 有色噪声干扰非平稳状态下，MVDR扩展凹槽滤波形成方法-With colored noise under non-steady state, the the MVDR extended groove filter forming method
EMD
- EMD 适合非线性、动态数据分析，基于数据本身，把数据分解为IMF，从高频到低频-empirical mode decomposition is useful to decompose nonlinear and nonstationary into several IMF from high frequency to low frquency.
UDEED
- 在集成学习中用UDEED算法来处理非平稳动态数据流中的分类，关于整体学习算法很有帮助，集成学习可以提高机器学习的推广！-In integrated learning using UDEED algorithm to handle the nonstationary dynamic data stream classification, machine learning promote integrated learning can improve the overall learning alg
nonstationary-deconvolution
- nonstationary seismic deconvolution