语音信号的短时分析，主要包括：分帧、短时能量、短时平均幅度、短时过零率、短时自相关函数、短时幅度差、倒谱、复倒谱、lpc系数、lpc谱估计等-Short-time speech signal analysis, mainly including: sub-frame, short-time energy, short-term average, short-time zero-crossing rate, short-time auto-correlation function, short-t

由AR模型参数得到功率谱。AR，MA和ARMA是功率谱估计中最主要的参数模型。AR模型的正则方程是一组线性方程。-The AR model parameters obtained power spectrum. AR, MA and ARMA power spectrum estimation is the most important parameter model. Canonical equations AR model is a set of linear equations.

用Cholesky分解求ARMA模型的参数并作谱估计.分两步，具体可参见《数字信号处理-理论、算法与实现》-Cholesky decomposition ask using ARMA model parameters and spectral estimation in two steps for more details, see. " Digital Signal Processing- Theory, Algorithm and Implementation"

用Welch平均法对信号 作功率谱估计.welch法又称之为加权交叠平均法。是一种经典的功率谱估计方法。-By Welch method to estimate the power spectrum signal .welch method is also known as the weighted average method overlap. It is a classic power spectrum estimation method.

基于听觉掩蔽效应的谱减法。掩蔽效应，有助于对音色、响度和音高的理解和估计。在语音增强和语音编码中，利用掩蔽效应改善输出语音质量已取得很大的效益，掩蔽效应也可用于改善电动汽车车内的来自电机的高频噪声问题。