匹配跟踪作为一种自适应的信号分解算法，为语音和音频正弦建模提供了一个新的框架。
As an adaptive algorithm of signal decomposition, matching pursuits provides a new framework for sinusoidal modeling of speech and audio signal.
经验模式分解（EMD）通过筛分过程将原始信号分解成若干个基本模式分量（IMF），可看作无需预设带宽的自适应高通滤波方法。
Empirical mode decomposition(EMD) is a signal processing technique to decompose data set into several intrinsic mode functions(IMF) by a sifting process.
在异步电机转子断条故障检测中，将原信号最优小波包分解，经自适应滤波后再信号重构。
During detection of asynchronous motor rotor-bar broken fault, the signal of current was reconstruct after decomposed by best wavelet packet and adaptive filtered.
该方法采用基于熵的小波包最佳基选取准则，对局部损伤信号进行自适应小波包分解，将分解结果显示于时—频空间即相平面上。
The L F signals are transformed by entropy based on wavelet packet algorithms for best basis selection, and the results are displayed in time- frequency space namely phase plane.
基于特征分解谱分析技术，提出并研究了在雷达中自适应抑制窄带数字通信信号干扰的方法。
An adaptive method applying eigenvalue decomposition-based spectrum analysis technique to narrow-band digital communication interference suppression in radar is proposed and studied.
在对带白噪声信号的小波变换特性分析的基础上，基于自相关函数的白噪声检验方法，提出了一种确定小波分解层数的自适应算法。
Threshold de-noising based on wavelet transform which is proposed to determine the decomposition order adaptively is an efficient method to reduce the white noise in the digital signal.
针对传统滤波方法处理非平稳信号的不足，提出利用经验模态分解法来处理转子启动信号，通过此方法的自适应滤波特性来提取这类信号中的低频分量。
This paper describes a method to extract the low frequency component from rotor startup signal based on empirical mode decomposition, which overcomes the difficulties of traditional filter methods.
与通常的自适应多用户检测算法相比，该算法利用了小波变换对小波空间进行了分解，信号经小波变换后自相关性会下降，收敛速度提高。
The algorithm makes use of wavelet transform to divide the wavelet space, which shows that the wavelet transform has a better decorrelation ability and leads to better convergence.
EMD分解法是一种自适应的信号处理方法，适用于分析非线性、非平稳过程。
The EMD method is a new method to analyze instability and nonlinearity.
它的分解基是随动态信号波形的变化而变化 ，具有自调节自适应的特征 ，因此能在时频域内描述非平稳非线性信号的局部特性。
Since the decomposition bases can vary with the local features of dynamic signals, the method is adaptive , therefore, highly efficient for describing nonlinear and non?stationary signals.
它的分解基是随动态信号波形的变化而变化 ，具有自调节自适应的特征 ，因此能在时频域内描述非平稳非线性信号的局部特性。
Since the decomposition bases can vary with the local features of dynamic signals, the method is adaptive , therefore, highly efficient for describing nonlinear and non?stationary signals.
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