自相关虽然能够判断清/浊音，并估计浊音的基音周期，但是其运算量很大，用乘法运算所需时间较长。为了避免乘法，采用短时平均幅度差函数（AMDF），验证AMDF与自相关有类似的作用，即平均幅度差函数能够代替自相关函数进行语音分析。-Although autocorrelation can differ from the Voiced/Unvoiced and voiced the pitch period estimate, but the operation is large enough, wit

详细说明：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 绝对保证质量，是保研后导师布置的一些基础程序-Details: short-time speech signal analysis, including: framing, short-term energy, short-term average rate, short-time zero crossing rate, short-time a

语音信号的时域、频域分析,包括短时能量分析、短时平均过零率、自相关函数、短时平均幅度差函数等。-Time-domain speech signal, frequency domain analysis, including short-term energy analysis, the average short-term zero-crossing rate, autocorrelation function, such as short-time average magnitude diff

计算语音信号的AMDF(短时平均幅度差)，基音估计(中心削波)，LBG算法的实现-Calculation of the speech signal AMDF (short time average magnitude difference), pitch estimation clipping (center), the implementation of the LBG algorithm

本压缩包是对语音信号的短时分析，包含短时能量、短时过零率、短时自相关以及短时平均幅度差等函数-The compressed package is a short-term analysis of the speech signal, comprising short-time energy, short-term zero-crossing rate, short-term as well as short-time average magnitude of the autocorrelatio