语音识别之梅尔频谱倒数MFCC（Mel Frequency Cepstrum Coefficient）
语音识别之梅尔频谱倒数MFCC（Mel Frequency Cepstrum Coefficient）
原理
- 梅尔频率倒谱系数：必定程度上模拟了人耳对语音的处理特色
- 预加剧：在语音信号中，高频部分的能量通常比较低，信号不利于处理，提升高频部分的能量能更好的处理
- 分帧：在比较短的时间内，语音信号不会发生突变，利于处理
- 加窗：帧内信号在后序FFT变换的时候不会出现端点突变的状况，较好地获得频谱
- 补零：FFT的要求输入数据须要知足2^k个点
- 计算能量谱：对语音信号最好的分析在其功率谱
- 计算梅尔频谱：梅尔频谱体现人耳对语音的特色
- 离散余弦变换：计算梅尔倒谱，易于观察
- 归一化：易于纵观整个语音信号的特色
过程
流程图：
从 人声的模拟信号 获得 MFCC的梅尔倒谱
ios
- 录音获得人声音频信号，保存到本地
%%
% r = audiorecorder(16000, 16, 1);
% record(r); % servel seconds
% stop(r);
% mySpeech = getaudiodata(r);
% figure;plot(mySpeech);title('mySpeech');
%%
mySpeech = wavread('mySpeech.wav', 'native');
figure;plot(mySpeech);title('mySpeech');
SizeOfmySpeech = size(mySpeech, 1);
for i = 2 : SizeOfmySpeech
mySpeech(i) = mySpeech(i) - 0.95 * mySpeech(i-1);
end
figure;plot(mySpeech);title('mySpeech_fix');
录音的要求是采用率为16000Hz，量化为16bit数据结构
- 读取本地语音文件
ret_value temp;
short waveData2[60000];
int main()
{
load_wave_file("mySpeech.wav", &temp, waveData2);
return 0;
}
总共有60000个采样点函数
- 设置窗函数（海明窗、汉宁窗、布拉克曼窗）
void setHammingWindow(float* frameWindow){
for(int i = 0; i < FRAMES_PER_BUFFER; i++){
frameWindow[i] = 0.54 - 0.46*cos(2 * PI * i / (FRAMES_PER_BUFFER - 1));
}
}
void setHanningWindow(float* frameWindow){
for(int i = 0; i < FRAMES_PER_BUFFER; i++){
frameWindow[i] = 0.5 - 0.5*cos(2 * PI * i / (FRAMES_PER_BUFFER - 1));
}
}
void setBlackManWindow(float* frameWindow){
for(int i = 0; i < FRAMES_PER_BUFFER; i++){
frameWindow[i] = 0.42 - 0.5*cos(2 * PI * i / (FRAMES_PER_BUFFER - 1))
+ 0.08*cos(4 * PI*i / (FRAMES_PER_BUFFER - 1));
}
}
这次选取的是海明窗spa
- 分帧加窗操做
// 加窗操做
int seg_shift = (i - 1) * NOT_OVERLAP;
for(j = 0; j < FRAMES_PER_BUFFER && (seg_shift + j) < numSamples; j++){
afterWin[j] = spreemp[seg_shift + j] * frameWindow[j];
}
每次分帧，数据点变为400个点3d
- 补零操做
// 知足FFT为2^n个点，补零操做
for(int k = j - 1; k < LEN_SPECTRUM; k++){
afterWin[k] = 0;
}
知足fft操做须要，补零至512个点code
- 计算能量谱
void FFT_Power(float* in, float* energySpectrum){
fftwf_complex* out = (fftwf_complex*)fftwf_malloc(sizeof(fftwf_complex)*LEN_SPECTRUM);
fftwf_plan p = fftwf_plan_dft_r2c_1d(LEN_SPECTRUM, in, out, FFTW_ESTIMATE);
fftwf_execute(p);
for(int i = 0; i < LEN_SPECTRUM; i++){
energySpectrum[i] = out[i][0] * out[i][0] + out[i][1] * out[i][1];
}
fftwf_destroy_plan(p);
fftwf_free(out);
}
这里用到了MIT大学的开源FFT变换库fftw3.h，快速计算能量谱(能够搜索下载根据本身的IDE配置)orm
- 计算梅尔谱
void computeMel(float* mel, int sampleRate, const float* energySpectrum){
int fmax = sampleRate / 2;
float maxMelFreq = 1125 * log(1 + fmax / 700);
// 计算频谱到梅尔谱的映射关系
for(int i = 0; i < NUM_FILTER + 2; i++){
m[i] = i*delta;
h[i] = 700 * (exp(m[i] / 1125) - 1);
f[i] = floor((256 + 1)*h[i] / sampleRate);
}
// 梅尔滤波
for(int i = 0; i < NUM_FILTER; i++){
for(int j = 0; j < 256; j++){
if(j >= melFilters[i][0] && j <= melFilters[i][1]){
mel[i] += ((j - melFilters[i][0]) / (melFilters[i][1] - melFilters[i][0]))*energySpectrum[j];
}
else if(j > melFilters[i][1] && j <= melFilters[i][2]){
mel[i] += ((melFilters[i][2] - j) / (melFilters[i][2] - melFilters[i][1]))*energySpectrum[j];
}
}
}
一共选择了40个三角滤波器，最后的梅尔谱也是40个点blog
- 计算梅尔倒谱
void DCT(const float* mel, float* melRec){
for(int i = 0; i < LEN_MELREC; i++){
for(int j = 0; j < NUM_FILTER; j++){
if(mel[j] <= -0.0001 || mel[j] >= 0.0001){
melRec[i] += log(mel[j])*cos(PI*i / (2 * NUM_FILTER)*(2 * j + 1));
}
}
}
}
把40个点的梅尔谱映射到13维的倒谱上。取对数作离散余弦变换内存
- 归一化处理
// 归一化处理
for(int i = 0; i < LEN_MELREC; i++){
sumMelRec[i] = sqrt(sumMelRec[i] / numFrames);
}
fstream fout("All_MelRec.txt", ios::out);
fstream fout2("All_MelRec_Bef.txt", ios::out);
for(int i = 0; i < numFrames; i++){
for(int j = 0; j < LEN_MELREC; j++){
fout2 << mulMelRec[i][j] << " ";
mulMelRec[i][j] /= sumMelRec[j];
fout << mulMelRec[i][j] << " ";
}
fout << endl;
fout2 << endl;
}
使得最终的结果数据聚拢，易于观察ci
- 绘图输出结果（以原始数据为例，和最终结果为例）
%% 读取原始音频文件
fidin = fopen('wavData.txt', 'r');
len_waveData = fscanf(fidin, '%d', 1);
waveData = zeros(len_waveData, 1);
for i = 1 : 1 : len_waveData
waveData(i) = fscanf(fidin, '%d', 1);
end
fclose(fidin);
subplot(2, 3, 1); plot(1:len_waveData, waveData);
axis([0 400 -2 2]);
title('原始音频文件');
%% 梅尔倒谱的色域
A = load('All_MelRec_Bef.txt');
figure;
imagesc(A'); hold on
colorbar;
title('梅尔倒谱的色域');
%% 梅尔倒谱的色域（归一化）
B = load('All_MelRec.txt');
figure;
imagesc(B'); hold on
colorbar;
title('梅尔倒谱的色域（归一化）');
其他输出操做是相同的，操做见最后的完整代码
结果
录音后的原始音频信号
总共有6000个采样点，量化为16bit，所以数据量级能达到10^4
MFCC操做中，第五帧的结果流程
原始音频分帧后，每一帧是400的点，从结果来看，在一帧的时间长度里面，数据变化不大，幅值维持在 [-1 1] 之间浮动。（如选取其余帧能够看到变化比较明显，看看原始音频就知道了）
加窗操做后，端点值被明显收敛到0，所以不会对能量谱的计算产生突变的状况。
能量谱和梅尔谱能够看出，与咱们已知的人声特色相关。
归一化以前的梅尔倒谱
高频能量集中在较低的维度，和能量谱的显示吻合
归一化的梅尔倒谱
归一化以后，相比未归一化的图，较高维度的能量可以较好地被分辨出来，易于分析
至此，梅尔倒谱工做完成。
完整代码
matlab录音文件 main.m
clear all
close all
clc
%%
% r = audiorecorder(16000, 16, 1);
% record(r); % servel seconds
% stop(r);
% mySpeech = getaudiodata(r);
% figure;plot(mySpeech);title('mySpeech');
%%
mySpeech = wavread('mySpeech.wav', 'native');
figure;plot(mySpeech);title('mySpeech');
SizeOfmySpeech = size(mySpeech, 1);
for i = 2 : SizeOfmySpeech
mySpeech(i) = mySpeech(i) - 0.95 * mySpeech(i-1);
end
figure;plot(mySpeech);title('mySpeech_fix');
C++主函数文件 main.cpp
#include<iostream>
#include "fftw3.h"
#include"MFCC.h"
#include"wav.h"
using namespace std;
int wavLen;
double waveData[60000];
ret_value temp;
short waveData2[60000];
int main()
{
/*wavLen = wavread("mySpeech.txt", waveData);
if(wavLen == -1)
exit(0);*/
load_wave_file("mySpeech.wav", &temp, waveData2);
MFCC(waveData2, 60000, 16000);
system("pause");
return 0;
}
C++音频定义头文件 wav.h
#ifndef _WAV_H
#define _WAV_H
#define MAXDATA (512*400) //通常采样数据大小,语音文件的数据不能大于该数据
#define SFREMQ (16000) //采样数据的采样频率8khz
#define NBIT 16
typedef struct WaveStruck{//wav数据结构
//data head
struct HEAD{
char cRiffFlag[4];
int nFileLen;
char cWaveFlag[4];//WAV文件标志
char cFmtFlag[4];
int cTransition;
short nFormatTag;
short nChannels;
int nSamplesPerSec;//采样频率,mfcc为8khz
int nAvgBytesperSec;
short nBlockAlign;
short nBitNumPerSample;//样本数据位数，mfcc为12bit
} head;
//data block
struct BLOCK{
char cDataFlag[4];//数据标志符(data)
int nAudioLength;//采样数据总数
} block;
} WAVE;
int wavread(char* filename, double* destination);
struct ret_value
{
char *data;
unsigned long size;
ret_value()
{
data = 0;
size = 0;
}
};
void load_wave_file(char *fname, struct ret_value *ret, short* waveData2);
#endif
C++音频实现文件 wav.cpp
#include"wav.h"
#include<cstdio>
#include<cstring>
#include<malloc.h>
int wavread(char* filename, double* destination){
WAVE wave[1];
FILE * f;
f = fopen(filename, "rb");
if(!f)
{
printf("Cannot open %s for reading\n", filename);
return -1;
}
//读取wav文件头而且分析
fread(wave, 1, sizeof(wave), f);
if(wave[0].head.cWaveFlag[0] == 'W'&&wave[0].head.cWaveFlag[1] == 'A'
&&wave[0].head.cWaveFlag[2] == 'V'&&wave[0].head.cWaveFlag[3] == 'E')//判断是不是wav文件
{
printf("It's not .wav file\n");
return -1;
}
if(wave[0].head.nSamplesPerSec != SFREMQ || wave[1].head.nBitNumPerSample != NBIT)//判断是否采样频率是16khz,16bit量化
{
printf("It's not 16khz and 16 bit\n");
return -1;
}
if(wave[0].block.nAudioLength>MAXDATA / 2)//wav文件不能太大,为sample长度的一半
{
printf("wav file is to long\n");
return -1;
}
//读取采样数据
fread(destination, sizeof(char), wave[0].block.nAudioLength, f);
fclose(f);
return wave[0].block.nAudioLength;
}
void load_wave_file(char *fname, struct ret_value *ret, short* waveData2)
{
FILE *fp;
fp = fopen(fname, "rb");
if(fp)
{
char id[5]; // 5个字节存储空间存储'RIFF'和'\0'，这个是为方便利用strcmp
unsigned long size; // 存储文件大小
short format_tag, channels, block_align, bits_per_sample; // 16位数据
unsigned long format_length, sample_rate, avg_bytes_sec, data_size; // 32位数据
fread(id, sizeof(char), 4, fp); // 读取'RIFF'
id[4] = '\0';
if(!strcmp(id, "RIFF"))
{
fread(&size, sizeof(unsigned long), 1, fp); // 读取文件大小
fread(id, sizeof(char), 4, fp); // 读取'WAVE'
id[4] = '\0';
if(!strcmp(id, "WAVE"))
{
fread(id, sizeof(char), 4, fp); // 读取4字节 "fmt ";
fread(&format_length, sizeof(unsigned long), 1, fp);
fread(&format_tag, sizeof(short), 1, fp); // 读取文件tag
fread(&channels, sizeof(short), 1, fp); // 读取通道数目
fread(&sample_rate, sizeof(unsigned long), 1, fp); // 读取采样率大小
fread(&avg_bytes_sec, sizeof(unsigned long), 1, fp); // 读取每秒数据量
fread(&block_align, sizeof(short), 1, fp); // 读取块对齐
fread(&bits_per_sample, sizeof(short), 1, fp); // 读取每同样本大小
fread(id, sizeof(char), 4, fp); // 读入'data'
fread(&data_size, sizeof(unsigned long), 1, fp); // 读取数据大小
ret->size = data_size;
ret->data = (char*)malloc(sizeof(char)*data_size); // 申请内存空间
//fread(ret->data, sizeof(char), data_size, fp); // 读取数据
fread(waveData2, sizeof(short), data_size, fp); // my fix
}
else
{
printf("Error: RIFF file but not a wave file\n");
}
}
else
{
printf("Error: not a RIFF file\n");
}
}
}
C++实现MFCC.h
#ifndef _MFCC_H #define _MFCC_H #define FRAMES_PER_BUFFER 400 #define NOT_OVERLAP 200 #define NUM_FILTER 40 #define PI 3.1415926 #define LEN_SPECTRUM 512 #define LEN_MELREC 13 void MFCC(const short* waveData, int numSamples, int sampleRate); void preEmphasizing(const short* waveData, float* spreemp, int numSamples, float heavyFactor); void setHammingWindow(float* frameWindow); void setHanningWindow(float* frameWindow); void setBlackManWindow(float* frameWindow); void FFT_Power(float* in, float* energySpectrum); void computeMel(float* mel, int sampleRate, const float* energySpectrum); void DCT(const float* mel, float* melRec); #endif
C++实现MFCC.cpp
#include"MFCC.h"
#include"fftw3.h"
#include<cmath>
#include<cstring>
#include<fstream>
#include<string>
using namespace std;
template<class T> void print_Array(T* arr, int len, string filename);
#define TORPINT true
#define PRINT_FRAME 100
float mulMelRec[500][LEN_MELREC];
void MFCC(const short* waveData, int numSamples, int sampleRate){
if(TORPINT) print_Array(waveData, 60000, "wavDataAll.txt");
// 预加剧
float* spreemp = new float[numSamples];
preEmphasizing(waveData, spreemp, numSamples, -0.95);
if(TORPINT) print_Array(waveData, 60000, "spreempAll.txt");
// 计算帧的数量
int numFrames = ceil((numSamples - FRAMES_PER_BUFFER) / NOT_OVERLAP) + 1;
// 申请内存
float* frameWindow = new float[FRAMES_PER_BUFFER];
float* afterWin = new float[LEN_SPECTRUM];
float* energySpectrum = new float[LEN_SPECTRUM];
float* mel = new float[NUM_FILTER];
float* melRec = new float[LEN_MELREC];
/*float** mulMelRec = new float*[numFrames + 200];
for(int i = 0; i < numFrames; i++){
mulMelRec[i] = new float[LEN_MELREC];
}*/
float* sumMelRec = new float[LEN_MELREC];
memset(sumMelRec, 0, sizeof(float)*LEN_MELREC);
memset(mulMelRec, 0, sizeof(float)*numFrames*LEN_MELREC);
// 设置窗参数
setHammingWindow(frameWindow);
//setHanningWindow(frameWindow);
//setBlackManWindow(frameWindow);
// 帧操做
for(int i = 0; i < numFrames; i++){
if(TORPINT && i == PRINT_FRAME) print_Array(waveData, FRAMES_PER_BUFFER, "wavData.txt");
if(TORPINT && i == PRINT_FRAME) print_Array(waveData, FRAMES_PER_BUFFER, "spreemp.txt");
int j;
// 加窗操做
int seg_shift = (i - 1) * NOT_OVERLAP;
for(j = 0; j < FRAMES_PER_BUFFER && (seg_shift + j) < numSamples; j++){
afterWin[j] = spreemp[seg_shift + j] * frameWindow[j];
}
// 知足FFT为2^n个点，补零操做
for(int k = j - 1; k < LEN_SPECTRUM; k++){
afterWin[k] = 0;
}
if(TORPINT && i == PRINT_FRAME)
print_Array(afterWin, LEN_SPECTRUM, "After.txt");
// 计算能量谱
FFT_Power(afterWin, energySpectrum);
if(TORPINT && i == PRINT_FRAME)
print_Array(energySpectrum, LEN_SPECTRUM, "energySpectrum.txt");
// 计算梅尔谱
memset(mel, 0, sizeof(float)*NUM_FILTER);
computeMel(mel, sampleRate, energySpectrum);
if(TORPINT && i == PRINT_FRAME)
print_Array(mel, NUM_FILTER, "mel.txt");
// 计算离散余弦变换
memset(melRec, 0, sizeof(float)*LEN_MELREC);
DCT(mel, melRec);
if(TORPINT && i == PRINT_FRAME)
print_Array(melRec, LEN_MELREC, "melRec.txt");
// 累计总值
for(int p = 0; p < LEN_MELREC; p++){
mulMelRec[i][p] = melRec[p];
sumMelRec[p] += melRec[p] * melRec[p];
}
}
// 归一化处理
for(int i = 0; i < LEN_MELREC; i++){
sumMelRec[i] = sqrt(sumMelRec[i] / numFrames);
}
fstream fout("All_MelRec.txt", ios::out);
fstream fout2("All_MelRec_Bef.txt", ios::out);
for(int i = 0; i < numFrames; i++){
for(int j = 0; j < LEN_MELREC; j++){
fout2 << mulMelRec[i][j] << " ";
mulMelRec[i][j] /= sumMelRec[j];
fout << mulMelRec[i][j] << " ";
}
fout << endl;
fout2 << endl;
}
fout.close();
fout2.close();
// 释放内存
delete[] spreemp;
delete[] frameWindow;
delete[] afterWin;
delete[] energySpectrum;
delete[] mel;
delete[] melRec;
delete[] sumMelRec;
/*for(int i = 0; i < LEN_MELREC; i++){
delete[] mulMelRec[i];
}
delete[] mulMelRec;*/
}
void preEmphasizing(const short* waveData, float* spreemp, int numSamples, float heavyFactor){
spreemp[0] = (float)waveData[0];
for(int i = 1; i < numSamples; i++){
spreemp[i] = waveData[i] + heavyFactor * waveData[i - 1];
}
}
void setHammingWindow(float* frameWindow){
for(int i = 0; i < FRAMES_PER_BUFFER; i++){
frameWindow[i] = 0.54 - 0.46*cos(2 * PI * i / (FRAMES_PER_BUFFER - 1));
}
}
void setHanningWindow(float* frameWindow){
for(int i = 0; i < FRAMES_PER_BUFFER; i++){
frameWindow[i] = 0.5 - 0.5*cos(2 * PI * i / (FRAMES_PER_BUFFER - 1));
}
}
void setBlackManWindow(float* frameWindow){
for(int i = 0; i < FRAMES_PER_BUFFER; i++){
frameWindow[i] = 0.42 - 0.5*cos(2 * PI * i / (FRAMES_PER_BUFFER - 1))
+ 0.08*cos(4 * PI*i / (FRAMES_PER_BUFFER - 1));
}
}
void FFT_Power(float* in, float* energySpectrum){
fftwf_complex* out = (fftwf_complex*)fftwf_malloc(sizeof(fftwf_complex)*LEN_SPECTRUM);
fftwf_plan p = fftwf_plan_dft_r2c_1d(LEN_SPECTRUM, in, out, FFTW_ESTIMATE);
fftwf_execute(p);
for(int i = 0; i < LEN_SPECTRUM; i++){
energySpectrum[i] = out[i][0] * out[i][0] + out[i][1] * out[i][1];
}
fftwf_destroy_plan(p);
fftwf_free(out);
}
void computeMel(float* mel, int sampleRate, const float* energySpectrum){
int fmax = sampleRate / 2;
float maxMelFreq = 1125 * log(1 + fmax / 700);
int delta = (int)(maxMelFreq / (NUM_FILTER + 1));
// 申请空间
float** melFilters = new float*[NUM_FILTER];
for(int i = 0; i < NUM_FILTER; i++){
melFilters[i] = new float[3];
}
float* m = new float[NUM_FILTER + 2];
float* h = new float[NUM_FILTER + 2];
float* f = new float[NUM_FILTER + 2];
// 计算频谱到梅尔谱的映射关系
for(int i = 0; i < NUM_FILTER + 2; i++){
m[i] = i*delta;
h[i] = 700 * (exp(m[i] / 1125) - 1);
f[i] = floor((256 + 1)*h[i] / sampleRate);
}
// 计算梅尔滤波参数
for(int i = 0; i < NUM_FILTER; i++){
for(int j = 0; j < 3; j++){
melFilters[i][j] = f[i + j];
}
}
// 梅尔滤波
for(int i = 0; i < NUM_FILTER; i++){
for(int j = 0; j < 256; j++){
if(j >= melFilters[i][0] && j <= melFilters[i][1]){
mel[i] += ((j - melFilters[i][0]) / (melFilters[i][1] - melFilters[i][0]))*energySpectrum[j];
}
else if(j > melFilters[i][1] && j <= melFilters[i][2]){
mel[i] += ((melFilters[i][2] - j) / (melFilters[i][2] - melFilters[i][1]))*energySpectrum[j];
}
}
}
// 释放内存
for(int i = 0; i < 3; i++){
delete[] melFilters[i];
}
delete[] melFilters;
delete[] m;
delete[] h;
delete[] f;
}
void DCT(const float* mel, float* melRec){
for(int i = 0; i < LEN_MELREC; i++){
for(int j = 0; j < NUM_FILTER; j++){
if(mel[j] <= -0.0001 || mel[j] >= 0.0001){
melRec[i] += log(mel[j])*cos(PI*i / (2 * NUM_FILTER)*(2 * j + 1));
}
}
}
}
template<class T>
void print_Array(T* arr, int len, string filename){
fstream fout(filename, ios::out);
fout << len << endl;
for(int i = 0; i < len; i++){
fout << arr[i] << " ";
}
fout << endl;
fout.close();
return;
}
Matlab实现输出观察文件 Matlab_print.m
clear all
close all
clc
%% 原始音频全部
fidin = fopen('wavDataAll.txt', 'r');
len_waveData = fscanf(fidin, '%d', 1);
waveData = zeros(len_waveData, 1);
for i = 1 : 1 : len_waveData
waveData(i) = fscanf(fidin, '%d', 1);
end
fclose(fidin);
subplot(2, 3, 1); plot(1:len_waveData, waveData);
title('原始音频文件');
fidin = fopen('spreempAll.txt', 'r');
len_spreemp = fscanf(fidin, '%d', 1);
spreemp = zeros(len_spreemp, 1);
for i = 1 : 1 : len_spreemp
spreemp(i) = fscanf(fidin, '%d', 1);
end
fclose(fidin);
subplot(2, 3, 2); plot(1:len_spreemp, waveData);
title('预加剧音频文件');
figure;
%% 读取原始音频文件
fidin = fopen('wavData.txt', 'r');
len_waveData = fscanf(fidin, '%d', 1);
waveData = zeros(len_waveData, 1);
for i = 1 : 1 : len_waveData
waveData(i) = fscanf(fidin, '%d', 1);
end
fclose(fidin);
subplot(2, 3, 1); plot(1:len_waveData, waveData);
axis([0 400 -2 2]);
title('原始音频文件');
%% 读取预加剧的音频
fidin = fopen('spreemp.txt', 'r');
len_spreemp = fscanf(fidin, '%d', 1);
spreemp = zeros(len_spreemp, 1);
for i = 1 : 1 : len_spreemp
spreemp(i) = fscanf(fidin, '%d', 1);
end
fclose(fidin);
subplot(2, 3, 2); plot(1:len_spreemp, waveData);
axis([0 400 -2 2]);
title('预加剧音频文件');
%% 加窗操做
fidin = fopen('After.txt', 'r');
len_AfterWin = fscanf(fidin, '%d', 1);
AfterWin = zeros(len_AfterWin, 1);
for i = 1 : 1 : len_AfterWin
AfterWin(i) = fscanf(fidin, '%f', 1);
end
fclose(fidin);
subplot(2, 3, 3); plot(1:len_AfterWin, AfterWin); grid on
title('加窗操做');
%% 能量谱
fidin = fopen('energySpectrum.txt', 'r');
len_energySpectrum = fscanf(fidin, '%d', 1);
energySpectrum = zeros(len_energySpectrum, 1);
for i = 1 : 1 : len_energySpectrum
energySpectrum(i) = fscanf(fidin, '%f', 1);
end
fclose(fidin);
subplot(2, 3, 4); plot(1:len_energySpectrum, energySpectrum); grid on
title('能量谱');
%% 梅尔谱
fidin = fopen('mel.txt', 'r');
len_mel = fscanf(fidin, '%d', 1);
mel = zeros(len_mel, 1);
for i = 1 : 1 : len_mel
mel(i) = fscanf(fidin, '%f', 1);
end
fclose(fidin);
subplot(2, 3, 5); plot(1:len_mel, mel); grid on
title('梅尔谱');
%% 梅尔倒谱
fidin = fopen('melRec.txt', 'r');
len_melRec = fscanf(fidin, '%d', 1);
melRec = zeros(len_melRec, 1);
for i = 1 : 1 : len_melRec
melRec(i) = fscanf(fidin, '%f', 1);
end
fclose(fidin);
subplot(2, 3, 6); stem(1:len_melRec, melRec); grid on
title('梅尔倒谱');
%% 梅尔倒谱的色域
A = load('All_MelRec_Bef.txt');
figure;
imagesc(A'); hold on
colorbar;
title('梅尔倒谱的色域');
%% 梅尔倒谱的色域（归一化）
B = load('All_MelRec.txt');
figure;
imagesc(B'); hold on
colorbar;
title('梅尔倒谱的色域（归一化）');
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