标签：离散 1.2 pre 1.7 4.4 性能 结合 个数 一个
在语音识别（SpeechRecognition）和话者识别（SpeakerRecognition）方面，最常用到的语音特征就是梅尔倒谱系数(Mel-scaleFrequency Cepstral Coefficients)，简称MFCC，根据人耳听觉机理的研究发现，人耳对不同频率的声波有不同的听觉敏感度。从200Hz到5000Hz的语音信号对语音的清晰度影响对大。两个响度不等的声音作用于人耳时，则响度较高的频率成分的存在会影响到对响度较低的频率成分的感受，使其变得不易察觉，这种现象称为掩蔽效应。由于频率较低的声音在内耳蜗基底膜上行波传递的距离大于频率较高的声音，故一般来说，低音容易掩蔽高音，而高音掩蔽低音较困难。在低频处的声音掩蔽的临界带宽较高频要小。所以，人们从低频到高频这一段频带内按临界带宽的大小由密到疏安排一组带通滤波器，对输入信号进行滤波。将每个带通滤波器输出的信号能量作为信号的基本特征，对此特征经过进一步处理后就可以作为语音的输入特征。由于这种特征不依赖于信号的性质，对输入信号不做任何的假设和限制，又利用了听觉模型的研究成果。因此，这种参数比基于声道模型的LPCC相比具有更好的鲁邦性，更符合人耳的听觉特性，而且当信噪比降低时仍然具有较好的识别性能。
梅尔倒谱系数（Mel-scale Frequency Cepstral Coefficients，简称MFCC）是在Mel标度频率域提取出来的倒谱参数，Mel标度描述了人耳频率的非线性特性，它与频率的关系可用下式近似表示
Mel(f)=2595 * lg(1 + f/700)
上式中，f表示频率，单位为赫兹
提取MFCC主要有以下几个步骤：
（1）对语音进行预加重
其实质是将语音信号通过一个高通滤波器。其目的是为了提升高频部分，使信号频谱变得平坦，保持在低频到高频的整个频带中，能够用同样的信噪比求频谱。
（2）分帧
一般而言，语音是非平稳信号，因为对语音信号分帧，一般取20-30ms,在这个时间内，语音信号可以看成是短时平稳的。同时为了避免相邻两帧的变化过大，因而会在两帧之间设置重复帧。让语音平稳过渡，避免丢失信息
（3）加窗
将每一帧乘以汉明窗，以增加帧左端和右端的连续性
（4）快速傅里叶变换
由于信号在时域上的变换通常很难看出信号的特性，所以通常将它转换为频域上的能量分布来观察，不同的能量分布，就能代表不同语音的特性。所以在乘上汉明窗后，每帧还必须再经过快速傅里叶变换以得到在频谱上的能量分布。对分帧加窗后的各帧信号进行快速傅里叶变换得到各帧的频谱
（5）通过带通滤波器
将能量谱通过一组Mel尺度的三角形滤波器组，定义一个有M个滤波器的滤波器组（滤波器的个数和临界带的个数相近），采用的滤波器为三角滤波器，中心频率为 。M通常取22-26。各f(m)之间的间隔随着m值的减小而缩小，随着m值的增大而增宽
三角滤波器的频率响应定义为
（6）对数运算
（7）经离散余弦变换（DCT）得到MFCC系数
（8）差分系数提取
标准的倒谱参数MFCC只反映了语音参数的静态特性，语音的动态特性可以用这些静态特征的差分谱来描述。实验证明：把动、静态特征结合起来才能有效提高系统的识别性能。差分参数的计算可以采用下面的公式
式中,dt表示第t个一阶差分；Ct表示第t个倒谱系数；Q表示倒谱系数的阶数；K表示一阶导数的时间差，可取1或2。将上式中结果再代入就可以得到二阶差分的参数。
MFCC的全部组成其实是由：
N维MFCC参数（N/3MFCC系数+ N/3一阶差分参数+ N/3二阶差分参数）
（9）利用python中的librosa计算梅尔倒谱系数
import librosa
filename = ‘bluesky3.wav‘
y,sr = librosa.load(filename,sr=None)
mfccs = librosa.feature.mfcc(y=y,sr=sr,n_mfcc=24)
print(mfccs)
提取出来的系数如下
[[-5.16945902e+02 -5.19426873e+02 -5.17804359e+02 -5.20445982e+02
-3.92014399e+02 -1.99430514e+02 -1.32414391e+02 -1.21530814e+02
-1.73034846e+02 -2.66531853e+02 -2.85621070e+02 -2.63104938e+02
-2.31941622e+02 -1.76323968e+02 -1.72509653e+02 -2.56427131e+02
-3.90704323e+02 -4.40807898e+02 -4.97732967e+02 -5.49958586e+02
-2.25280049e+02 -1.10516672e+02 -9.07831003e+01 -1.15373407e+02
-1.87336026e+02 -2.85299787e+02 -2.94394641e+02 -2.44495864e+02
-2.06237194e+02 -1.86611565e+02 -2.10363761e+02 -2.47665943e+02
-3.08757357e+02 -4.47517093e+02 -4.93997607e+02 -5.07980432e+02
-5.16757926e+02 -5.32005739e+02]
[ 5.11134806e+01 6.91720700e+01 7.15531048e+01 7.12734335e+01
9.08245135e+01 1.01497819e+02 8.12186048e+01 8.33730312e+01
1.08621350e+02 1.28762874e+02 7.21848638e+01 1.55431938e+01
2.95236626e+01 4.94096653e+01 4.78561391e+01 6.13353705e+01
8.69647900e+01 8.82910649e+01 9.44872693e+01 9.26345388e+01
1.04690064e+02 8.49141780e+01 7.04109228e+01 6.94721917e+01
6.59353131e+01 6.04523888e+01 7.53665232e+01 4.29252982e+01
2.93457058e+01 5.04983562e+01 8.20349143e+01 1.10141408e+02
9.62271578e+01 1.04033766e+02 7.28028666e+01 5.50120347e+01
6.01528641e+01 8.82767754e+01]
[ 4.40124295e+01 4.94963175e+01 5.03965598e+01 4.96368533e+01
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-9.92229505e+00 3.12881302e+01 5.26448209e+01 6.18872674e+01
7.12667095e+01 5.13199701e+01 3.78888413e+01 4.25996922e+01
4.59955699e+01 4.57416981e+01 4.89163846e+01 4.85522111e+01
-3.29719643e+01 -3.80206753e+01 -2.03933844e+01 3.48298070e+01
7.76437551e+01 8.77171254e+01 9.80018885e+01 9.96103532e+01
8.51518495e+01 8.12490617e+01 6.66983819e+01 3.29080376e+01
2.64116607e+01 5.99595999e+01 5.00281376e+01 4.34092514e+01
4.72294125e+01 5.31052658e+01]
[ 2.08999032e+01 2.15156881e+01 2.28291669e+01 2.15887638e+01
3.85014549e+01 8.93286749e+00 -1.18995596e+00 6.28643275e+00
2.05549272e+01 1.81415569e+01 4.06337256e+00 1.61541417e+01
1.58121707e+01 2.62781012e+00 2.85583435e+00 1.08634033e+01
1.96407986e+01 1.98154827e+01 2.24381440e+01 2.51481243e+01
1.40064019e+00 6.68123093e+00 8.89964808e+00 1.03448077e+01
1.11054949e+01 2.06248289e+01 4.27228419e+01 4.04418069e+01
1.62139896e+01 3.78174306e+00 9.42321227e+00 2.36316470e+01
2.09207680e+01 1.55319521e+01 2.40119615e+01 2.16789348e+01
2.25692945e+01 2.40427699e+01]
[ 1.42081261e+01 1.19954047e+01 1.26300844e+01 1.17705268e+01
1.74204536e+00 6.65325828e+00 1.15978172e+01 1.76269932e+01
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-1.87064922e+00 6.47606376e+00 1.27196742e+01 1.91020167e+01
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-2.94856650e+01 -2.54366883e+01 -1.41270654e+01 2.26676537e+00
6.65831442e+00 6.14380426e+00 9.43607733e+00 8.52052202e+00
1.18612038e+01 1.02286928e+01]
[ 3.02039535e+01 2.93145994e+01 2.60726758e+01 2.88239396e+01
6.87383913e+00 7.93759317e+00 8.98631230e+00 9.11187104e+00
1.62771213e+00 9.55561273e+00 1.16049523e+01 5.27004267e+00
-1.53694849e+01 -2.03309793e+00 2.90698571e+00 5.80393596e+00
1.93766652e+01 2.95971255e+01 3.39015376e+01 2.56393392e+01
1.66322901e+01 1.84797438e+01 1.11237945e+01 3.16948292e+00
3.74167343e+00 3.28186569e+00 2.01883404e+01 2.79781435e+01
2.18972604e+01 2.13154926e+01 2.19232341e+01 4.00466213e+00
3.93892473e+00 2.50574136e+01 3.24725384e+01 3.11426502e+01
3.26689499e+01 2.67350829e+01]
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1.32807251e+01 1.18246621e+01]
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-8.91745710e+00 -9.30122678e+00 -1.41623646e+01 -1.14175808e+01
-6.51262734e+00 -8.01185636e+00]
[-4.14329747e+00 -5.93870926e+00 -3.64808044e+00 -5.26830372e+00
-1.04941311e+01 -1.15952438e+01 -1.65578359e+01 -2.64320469e+01
-3.21789512e+01 -3.43551795e+01 -2.25116579e+01 -1.14651478e+01
-2.31085283e+01 -2.06553776e+01 -1.31897089e+01 -1.10629150e+01
-2.11010484e+01 -2.47115213e+01 -2.03069248e+01 -8.34855515e+00
6.66756525e+00 -2.73821709e+00 -5.88103484e+00 -1.18373884e+01
-1.75935451e+01 -1.78831314e+01 -1.48751956e+01 -1.49180380e+01
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-2.93920574e+01 -2.92496423e+01 -5.97949994e+00 -3.99912776e+00
-5.61361341e+00 -8.52498488e+00]
[-1.02602140e+01 -1.34176744e+01 -1.12112350e+01 -1.32207249e+01
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-1.21328590e+01 -1.74254257e+01]
[ 5.63615077e+00 4.01710292e+00 2.87944953e+00 3.81178404e+00
8.24377901e+00 8.93584467e+00 1.90855522e+00 -1.19719193e+01
-1.73508427e+01 -9.53830187e+00 -6.38777646e+00 -1.04602752e+01
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-8.50749191e+00 -6.47553954e+00 -4.38135305e+00 -3.35847624e+00
2.32558432e+00 1.16516774e+00 -2.07215127e-01 -6.22413776e+00
-6.55814404e+00 -1.43284422e+01 -9.95259391e+00 -9.00736472e+00
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-1.18162263e+01 -4.95635672e+00 -6.18547249e+00 -1.26149751e+00
3.23408288e+00 6.64383928e-01]
[ 1.29362736e+01 1.27585928e+01 1.21614492e+01 1.22197423e+01
-4.02662661e-02 -2.83952655e+00 -5.51763258e+00 -1.60256376e+01
-1.69776872e+01 -5.70260756e+00 1.42317693e+00 1.55578426e+00
-3.72100940e+00 -3.43261417e+00 -1.23840990e+00 -2.27624915e+00
-5.64463037e+00 -7.74432314e+00 -9.63678857e+00 -2.87123740e+00
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1.90796193e+00 4.46368890e+00 -6.64704818e-02 -7.43267560e+00
-7.22656125e+00 2.72163772e-01 5.00228257e+00 1.05502517e+01
1.37700418e+01 9.39637925e+00]
[-1.66918740e+01 -1.00785471e+01 -1.11726195e+01 -9.41501851e+00
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-1.30561603e+01 -1.92052384e+00]
[-9.83918392e-01 6.23206421e-01 3.88571585e-01 2.12295800e-01
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[-7.91249533e+00 -8.54954059e+00 -6.83591651e+00 -8.16052034e+00
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参考链接：
（1）https://blog.csdn.net/qq_39516859/article/details/80679718
（2）https://blog.csdn.net/jojozhangju/article/details/18678861
（3）https://blog.csdn.net/zouxy09/article/details/9156785
标签：离散 1.2 pre 1.7 4.4 性能 结合 个数 一个
原文地址：https://www.cnblogs.com/tingweichen/p/9898837.html
