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MFCC的基础算法。 有很详细的代码。 注意阅读,提取自己需要的。测试过,可以用,欢迎新手学习。-MFCC based algorithm. A very detailed code. Note reading, extracting their own needs. Tested, can welcome newcomers to learn.
Update : 2024-05-13 Size : 81920 Publisher : jason

Othermfcc
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mfcc函数,用于在语音识别中提取MFCC参数特征-mfcc function, for the extraction of MFCC in speech recognition feature parameters
Update : 2024-05-13 Size : 1024 Publisher : 范恺

CSharpmfcc
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mfcc clacul ,extarction des cararcteristique de fichier audio
Update : 2024-05-13 Size : 3017728 Publisher : mmor

介绍语音识别中特征参数mfcc的改进算法,对于语音识别和语音合成有更好的效率-Introduction of speech recognition feature parameters mfcc improved algorithm for speech recognition and speech synthesis for better efficiency
Update : 2024-05-13 Size : 232448 Publisher : 路路

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MFCC特征提取算法以及语音端点检测源码-MFCC feature extraction algorithm and voice activity detection source
Update : 2024-05-13 Size : 50176 Publisher : zhou

语音识别有说话者识别和语义识别等,文章中是用MFCC实现的语义识别中的阿拉伯数字0-9的识别,以及结果。-Speech recognition with speaker recognition and semantic recognition, the article is realized with MFCC recognition of the Arabic numerals 0-9 semantic identification, and results.
Update : 2024-05-13 Size : 77824 Publisher : zhou

dtw文件是运用DTW算法实现安静环境下语音识别的。其中vad.m是端点检测程序;lpc.m是计算LPC参数的程序;lpc21lpcc.m是计算LPCC参数的程序;mfcc.m是计算MFCC参数的程序;dtw.m是实现经典DTW算法的程序;dtw2.m是实现高效DTW算法的程序,testdtw.m是最终测试程序,其中可以通过改变其中的特征参数名选择不同的特征参数。-dtw file DTW algorithm is to use speech recognition in quiet environments. Which is the endpoint detection process vad.m lpc.m is to calculate the LPC parameters of the program lpc21lpcc.m procedure is to calculate the LPCC parameters mfcc.m procedure is to calculate the MFCC parameters dtw.m the classic DTW algorithm to achieve the program dtw2.m DTW algorithm is to achieve efficient procedures, testdtw.m is the final test program, which can change the parameters in which different parameters were selected.
Update : 2024-05-13 Size : 8192 Publisher : 于军

dtw0文件是运用DTW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;lpc.m是计算LPC参数的程序;lpc21lpcc.m是计算LPCC参数的程序;mfcc.m是计算MFCC参数的程序;dtw.m是实现经典DTW算法的程序;dtw2.m是实现高效DTW算法的程序,testdtw.m是最终测试程序,其中可以通过改变其中的特征参数名选择不同的特征参数。-dtw0 file DTW algorithm is to use speech recognition in noisy environments. Which is the endpoint detection process vad.m lpc.m is to calculate the LPC parameters of the program lpc21lpcc.m procedure is to calculate the LPCC parameters mfcc.m procedure is to calculate the MFCC parameters dtw.m the classic DTW algorithm to achieve the program dtw2.m DTW algorithm is to achieve efficient procedures, testdtw.m is the final test program, which can change the parameters in which different parameters were selected.
Update : 2024-05-13 Size : 1084416 Publisher : 于军

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.
Update : 2024-05-13 Size : 538624 Publisher : 于军

端点检测论文: 基于LPCC、MFCC,基于双门限的端点检测-Endpoint detection paper: Based on the LPCC, MFCC, dual-threshold-based endpoint detection, etc.
Update : 2024-05-13 Size : 9939968 Publisher : youxi

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mfcc header file rot speaker recogntion implementation
Update : 2024-05-13 Size : 1024 Publisher : tamired

SCMimm3851
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This project describes the work done on the development of an audio segmentation and classification system. Many existing works on audio classification deal with the problem of classifying known homogeneous audio segments. In this work, audio recordings are divided into acoustically similar regions and classified into basic audio types such as speech, music or silence. Audio features used in this project include Mel Frequency Cepstral Coefficients (MFCC), Zero Crossing Rate and Short Term Energy (STE). These features were extracted from audio files that were stored in a WAV format. Possible use of features, which are extracted directly from MPEG audio files, is also considered. Statistical based methods are used to segment and classify audio signals using these features. The classification methods used include the General Mixture Model (GMM) and the k- Nearest Neighbour (k-NN) algorithms. It is shown that the system implemented achieves an accuracy rate of more than 95 for discrete audio classification.-This project describes the work done on the development of an audio segmentation and classification system. Many existing works on audio classification deal with the problem of classifying known homogeneous audio segments. In this work, audio recordings are divided into acoustically similar regions and classified into basic audio types such as speech, music or silence. Audio features used in this project include Mel Frequency Cepstral Coefficients (MFCC), Zero Crossing Rate and Short Term Energy (STE). These features were extracted from audio files that were stored in a WAV format. Possible use of features, which are extracted directly from MPEG audio files, is also considered. Statistical based methods are used to segment and classify audio signals using these features. The classification methods used include the General Mixture Model (GMM) and the k- Nearest Neighbour (k-NN) algorithms. It is shown that the system implemented achieves an accuracy rate of more than 95 for discrete audio classification.
Update : 2024-05-13 Size : 653312 Publisher : kvga

MFCC COMPENSATION FOR IMPROVED RECOGNITION OF FILTERED AND BANDLIMITED SPEECH
Update : 2024-05-13 Size : 208896 Publisher : kvga

用matlab仿真0到9十个数字的语音识别 1、对语音的WAV文件和LAB文件进行处理,产生十个文件,每个文件对应于一个数字,存贮着该数字的波形文件。(shujuzhengli.m) 2、分别利用上面十个文件训练出十个HMM模板,具体方法是:首先将语音的波形文件分帧,以128个点为一帧,帧移为64,每一帧通过mfcc.m计算出13个系数,随着波形的长度不同,一个语音文件可以计算得到13*N个系数,截取13*15的矩阵(mfcc系数)用作训练数据。一般一个HMM模板用20组mfcc系数训练,得到初始状态分布、状态转移矩阵、高斯正态分布的均值和方差以及混合矩阵,这就是该语音的特征,存贮下来,识别的时候使用。(trainmfcc.m) 3、识别过程:识别的前面部分与训练相似,都是要计算得到mfcc系数,不同在于,识别时,将计算得到的mfcc 参数分别代入训练得到的HMM模板求出概率,比较出最大概率者,则该模板对应的数字就是识别的数字。(shibiesb.m) 4、用大量语音文件做测试,结果正确率为90 以上。-Speech Recogonition: Mumber 0-9
Update : 2024-05-13 Size : 111616 Publisher : Fatso Ding

用c语言编写的语音特征参数的程序,该语音特征参数为mfcc参数,主要用于说话人识别-Written by c parameters of the process of speech, the speech feature parameter is mfcc parameters, mainly used for speaker recognition
Update : 2024-05-13 Size : 3055616 Publisher : zhangge

key word recognition using mfcc
Update : 2024-05-13 Size : 142336 Publisher : kumar

VC/MFCmfcc
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mfcc technique n algorithm1
Update : 2024-05-13 Size : 431104 Publisher : suhas

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best mfcc algorithm-best mfcc algorithm....
Update : 2024-05-13 Size : 724992 Publisher : suhas

matlabmfcc
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这是个基于matlab环境下,提取mfcc参数的小程序,希望能对大家有帮助。-This is a matlab-based environment, the applet parameter extraction mfcc, hoping to be helpful.
Update : 2024-05-13 Size : 1024 Publisher : 张春雷

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JAva implemntation of mfcc
Update : 2024-05-13 Size : 5120 Publisher : chrenuka
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