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mfcc_svm

于 2021-04-27 发布文件大小:10709KB

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下载积分: 1 下载次数: 8

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代码说明：

mfcc特征提取法以及svm训练可以使用(MFCC feature extraction method and SVM training can be used)

文件列表：

MFCC_2, 0 , 2018-04-27
MFCC_2\mfcc.m, 7190 , 2018-04-21
MFCC_2\trifbank.m, 4797 , 2018-04-21
MFCC_2\untitled.fig, 10707 , 2018-04-23
MFCC_2\untitled.m, 9006 , 2018-04-23
MFCC_2\Validation_test_set, 0 , 2018-04-27
MFCC_2\Validation_test_set\f1.wav, 914076 , 2018-04-21
MFCC_2\Validation_test_set\f2.wav, 983706 , 2018-04-21
MFCC_2\Validation_test_set\f3.wav, 998454 , 2018-04-21
MFCC_2\Validation_test_set\f4.wav, 919192 , 2018-04-21
MFCC_2\Validation_test_set\f5.wav, 1036958 , 2018-04-21
MFCC_2\Validation_test_set\f6.wav, 1190964 , 2018-04-21
MFCC_2\Validation_test_set\f7.wav, 1052724 , 2018-04-21
MFCC_2\Validation_test_set\f8.wav, 980224 , 2018-04-21
MFCC_2\Validation_test_set\m1.wav, 887246 , 2018-04-21
MFCC_2\Validation_test_set\m2.wav, 790168 , 2018-04-21
MFCC_2\Validation_test_set\m3.wav, 1046376 , 2018-04-21
MFCC_2\Validation_test_set\m4.wav, 929228 , 2018-04-21
MFCC_2\Validation_test_set\m5.wav, 1033064 , 2018-04-21
MFCC_2\Validation_test_set\m6.wav, 1061118 , 2018-04-21
MFCC_2\Validation_test_set\m7.wav, 952576 , 2018-04-21
MFCC_2\Validation_test_set\m8.wav, 907726 , 2018-04-21
MFCC_2\vec2frames.m, 6993 , 2018-04-21

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hmm
对隐马尔科夫工具包的使用及安装进行说明，主要是在matlab下运用

2010-01-10 12:11:10下载

积分：1
jmnhb
是信号处理的基础，完整的基于HMM的语音识别系统，外文资料里面的源代码。( Is the basis of the signal processing, Complete HMM-based speech recognition system, Foreign materials inside the source code.)

2017-04-17 15:26:58下载

积分：1
hmm
hmm文件时运用HMM算法实现噪声环境下语音识别的。其中vad.m是端点检测程序；mfcc.m是计算MFCC参数的程序；pdf.m函数是计算给定观察向量对该高斯概率密度函数的输出概率；mixture.m是计算观察向量对于某个HMM状态的输出概率，也就是观察向量对该状态的若干高斯混合元的输出概率的线性组合；getparam.m函数是计算前向概率、后向概率、标定系数等参数；viterbi.m是实现Viterbi算法；baum.m是实现Baum-Welch算法；inithmm.m是初始化参数；train.m是训练程序；main.m是训练程序的脚本文件；recog.m是识别程序。(hmm HMM algorithm file using speech recognition in noisy environments. Which is the endpoint detection process vad.m mfcc.m procedure is to calculate the MFCC parameters pdf.m function is calculated for a given observation vector of the Gaussian probability density function of output probability mixture.m is to calculate the observation vector for a HMM state output probability of observation vector is the number of Gaussian mixture per state output probability of the linear combination getparam.m before the calculation of the probability function, backward probability, calibration coefficients and other parameters viterbi.m is Viterbi algorithm implementation baum.m Baum-Welch algorithm to achieve inithmm.m is the initialization parameters train.m is the training program main.m training program is a script file recog.m is to identify procedures.)

2010-09-16 20:51:49下载

积分：1
zengqiang
语音增强算法综述，还可以，基本的谱减法，维纳滤波法都有(Overview of speech enhancement algorithm, can also basic spectral subtraction, Wiener filtering method has)

2021-05-13 13:30:02下载

积分：1
dtw_revised
利用DTW模版匹配算法实现0~9十个数字的识别。(use DTW template matching algorithm 0-9 10-digit identification.)

2007-06-11 16:51:28下载

积分：1
20080426
信息融合方面得例题，对已知要求的目标，利用动态矩阵控制（DMC）使其达到期望值。所有程序均已通过调试。希望对大家有所帮助(Example of information integration have on the known requirements of the target, the use of Dynamic Matrix Control (DMC) to reach the expectations. All have been through the debugging process. I hope all of you to help)

2008-04-26 13:07:28下载

积分：1
blockframes
语音信号处理中用于说话认识别中预处理部分的分帧函数代码(Speech signal processing for speech understanding in other parts of the sub-frame in the pre-function code)

2011-05-17 14:30:42下载

积分：1
wden1.rar
优化小波阈值，对硬阈值和软阈值的改进，通常用于语音信号增强，去噪(Optimization of wavelet threshold, the hard threshold and soft threshold improvement, commonly used in speech signal enhancement and denoise)

2020-12-02 18:29:26下载

积分：1
最终代码说话人识别
实现了基于特定话语的聚类LGB和VQ的说话人识别(Speaker Recognition Based on Clustering LGB and VQ)

2019-05-20 15:04:44下载

积分：1
kaerman
语音信号去噪，在MATLAB开发环境下，对含噪声的语音信号进行卡尔曼滤波，实现语音增强的功能。(The voice signal denoising, the Kalman filter, the noisy speech signal in the MATLAB development environment Wave, speech enhancement.)

2012-03-15 10:25:29下载

积分：1

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