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[Speech/Voice recognition/combine] Energynormalization DL : 0: Speech Recognition - Numbers 1 to 5 Energy normalization and time alignment References: [1] L. Rabiner and B.H. Juang,Fundamentals of Speech Recognition, Prentice-Hall, 1993. ％ [2] P.E. Papamichalis, Practical Approaches to Speech Coding, Prentice-Hall, 1987. ％ [3] J.D. Markel and A.H. Gray,Linear Prediction of Speech-Speech Recognition-Numbers 1 to 5 Energy n ormalization and time alignment References : [1] L. Paras and B. H. Juang, Fundamentals of Speech Recognition. Prentice-Hall, 1993. % [2] P. E. Papamichalis, Practical Approaches to Speech Coding, Prentice-Hall, 1987. % [3] J. D. Markel and A. H. Gray, Linear Prediction of Speech
Date : 2008-10-13 Size : 1.41kb User : pan
[Speech/Voice recognition/combine] EnergyNormalizationCepSpec DL : 0: Speech Recognition - Numbers 1 to 5 Energy normalization and time alignment References: [1] L. Rabiner and B.H. Juang,Fundamentals of Speech Recognition, Prentice-Hall, 1993. ％ [2] P.E. Papamichalis, Practical Approaches to Speech Coding, Prentice-Hall, 1987. ％ [3] J.D. Markel and A.H. Gray,Linear Prediction of Speech-Speech Recognition-Numbers 1 to 5 Energy n ormalization and time alignment References : [1] L. Paras and B. H. Juang, Fundamentals of Speech Recognition. Prentice-Hall, 1993. % [2] P. E. Papamichalis, Practical Approaches to Speech Coding, Prentice-Hall, 1987. % [3] J. D. Markel and A. H. Gray, Linear Prediction of Speech
Date : 2008-10-13 Size : 2.15kb User : pan
[Speech/Voice recognition/combine] Energynormalization DL : 0: Speech Recognition - Numbers 1 to 5 Energy normalization and time alignment References: [1] L. Rabiner and B.H. Juang,Fundamentals of Speech Recognition, Prentice-Hall, 1993. ％ [2] P.E. Papamichalis, Practical Approaches to Speech Coding, Prentice-Hall, 1987. ％ [3] J.D. Markel and A.H. Gray,Linear Prediction of Speech-Speech Recognition-Numbers 1 to 5 Energy n ormalization and time alignment References : [1] L. Paras and B. H. Juang, Fundamentals of Speech Recognition. Prentice-Hall, 1993. % [2] P. E. Papamichalis, Practical Approaches to Speech Coding, Prentice-Hall, 1987. % [3] J. D. Markel and A. H. Gray, Linear Prediction of Speech
Date : 2025-12-16 Size : 1kb User : pan
[Speech/Voice recognition/combine] EnergyNormalizationCepSpec DL : 0: Speech Recognition - Numbers 1 to 5 Energy normalization and time alignment References: [1] L. Rabiner and B.H. Juang,Fundamentals of Speech Recognition, Prentice-Hall, 1993. ％ [2] P.E. Papamichalis, Practical Approaches to Speech Coding, Prentice-Hall, 1987. ％ [3] J.D. Markel and A.H. Gray,Linear Prediction of Speech-Speech Recognition-Numbers 1 to 5 Energy n ormalization and time alignment References : [1] L. Paras and B. H. Juang, Fundamentals of Speech Recognition. Prentice-Hall, 1993. % [2] P. E. Papamichalis, Practical Approaches to Speech Coding, Prentice-Hall, 1987. % [3] J. D. Markel and A. H. Gray, Linear Prediction of Speech
Date : 2025-12-16 Size : 2kb User : pan
[Speech/Voice recognition/combine] LMS DL : 0: LMS算法实现自适应滤波 clear close all clc N=10000 设置仿真长度信号产生参数设定 a1=-0.195 a1=-1.5955 a2=0.95 R0=[1,a1,a2 a1,1+a2,0 a2,a1,1] p=[1,0,0] r=inv(R0)*p 计算理论自相关函数 R=[r(1),r(2) r(2),r(1)] 生成理论自相关矩阵 p1=[r(2),r(3)] 生成互相关 h=inv(R)*p1 计算维纳解 Jmin=r(1)-h *p1 计算维纳解时最小均方误差 u=1/sum(eigs(R- LMS算法实现自适应滤波 clear close all clc N=10000 设置仿真长度信号产生参数设定 a1=-0.195 a1=-1.5955 a2=0.95 R0=[1,a1,a2 a1,1+a2,0 a2,a1,1] p=[1,0,0] r=inv(R0)*p 计算理论自相关函数 R=[r(1),r(2) r(2),r(1)] 生成理论自相关矩阵 p1=[r(2),r(3)] 生成互相关 h=inv(R)*p1 计算维纳解 Jmin=r(1)-h*p1 计算维纳解时最小均方误差 u=1/sum(eigs(R))
Date : 2025-12-16 Size : 1kb User : wan