C/C++实现librosa音频处理库melspectrogram和mfcc

C/C++实现librosa音频处理库melspectrogram和mfcc

目录

C/C++实现librosa音频处理库melspectrogram和mfcc

1.项目结构

2.依赖环境

3.C++ librosa音频处理库实现

(1) 对齐读取音频文件

(2) 对齐melspectrogram

(3) 对齐MFCC

4.Demo运行

5.librosa库C++源码下载

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深度学习语音处理中，经常要用到音频处理库librosa，奈何librosa目前仅有python版本；而语音识别算法开发中，经常要用到melspectrogram和MFCC这些音频信息，因此需要实现C/C++版本melspectrogram和MFCC；网上已经存在很多版本的C/C++的melspectrogram和MFCC，但测试发现跟Python的librosa的处理结果存在很大差异；经过多次优化测试，本项目实现了C/C++版本的音频处理库librosa中load、melspectrogram和mfcc的功能，项目基本完整对齐Pyhon音频处理库librosa三个功能：

• librosa.load：实现语音读取
• librosa.feature.melspectrogram：实现计算melspectrogram
• librosa.feature.mfcc：实现计算MFCC

【尊重原创，转载请注明出处】https://blog.csdn.net/guyuealian/article/details/132077896

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1.项目结构

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2.依赖环境

项目需要安装Python和C/C++相关的依赖包

Python依赖库，使用pip install即可

numpy==1.16.3
matplotlib==3.1.0
Pillow==6.0.0
easydict==1.9
opencv-contrib-python==4.5.2.52
opencv-python==4.5.1.48
pandas==1.1.5
PyYAML==5.3.1
scikit-image==0.17.2
scikit-learn==0.24.0
scipy==1.5.4
seaborn==0.11.2
tqdm==4.55.1
xmltodict==0.12.0
pybaseutils==0.7.6
librosa==0.8.1
pyaudio==0.2.11
pydub==0.23.1

C++依赖库，主要用到Eigen3和OpenCV

• Eigen3：用于矩阵计算，项目已经支持Eigen3，无须安装
• OpenCV： 用于显示图像，安装方法请参考Ubuntu18.04安装opencv和opencv_contrib

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3.C++ librosa音频处理库实现

(1) 对齐读取音频文件

Python中可使用librosa.load读取音频文件

data, sr = librosa.load(path, sr, mono)

Python实现读取音频文件：

# -*-coding: utf-8 -*-
import numpy as np
import librosadef read_audio(audio_file, sr=16000, mono=True):"""默认将多声道音频文件转换为单声道，并返回一维数组；如果你需要处理多声道音频文件，可以使用 mono=False,参数来保留所有声道，并返回二维数组。:param audio_file::param sr: sampling rate:param mono: 设置为true是单通道，否则是双通道:return:"""audio_data, sr = librosa.load(audio_file, sr=sr, mono=mono)audio_data = audio_data.T.reshape(-1)return audio_data, srdef print_vector(name, data):np.set_printoptions(precision=7, suppress=False)print("------------------------%s------------------------\n" % name)print("{}".format(data.tolist()))if __name__ == '__main__':sr = Noneaudio_file = "data/data_s1.wav"data, sr = read_audio(audio_file, sr=sr, mono=False)print("sr         = %d, data size=%d" % (sr, len(data)))print_vector("audio data", data)

 C/C++读取音频文件：需要根据音频的数据格式进行解码，参考：C语言解析wav文件格式 ，本项目已经实现C/C++版本的读取音频数据，可支持单声道和双声道音频数据(mono)

/*** 读取音频文件,目前仅支持wav格式文件* @param filename wav格式文件* @param out 输出音频数据* @param sr 输出音频采样率* @param mono 设置为true是单通道，否则是双通道* @return*/
int read_audio(const char *filename, vector<float> &out, int *sr, bool mono = true);

#include <iostream>
#include <vector>
#include <algorithm>
#include "librosa/audio_utils.h"
#include "librosa/librosa.h"using namespace std;int main() {int sr = -1;string audio_file = "../data/data_s1.wav";vector<float> data;int res = read_audio(audio_file.c_str(), data, &sr, false);if (res < 0) {printf("read wav file error: %s\n", audio_file.c_str());return -1;}printf("sr         = %d, data size=%d\n", sr, data.size());print_vector("audio data", data);return 0;
}

测试和对比Python和C++版本读取音频文件数据，经过多轮测试，二者的读取的音频数值差异已经很小，基本已经对齐python librosa库的librosa.load()函数 

	数值对比
C++版本
Python版本

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(2) 对齐melspectrogram

关于melspectrogram梅尔频谱的相关原理，请参考基于梅尔频谱的音频信号分类识别(Pytorch)

Python的librosa库的提供了librosa.feature.melspectrogram()函数，返回一个二维数组，可以使用OpenCV显示该图像

def librosa_feature_melspectrogram(y,sr=16000,n_mels=128,n_fft=2048,hop_length=256,win_length=None,window="hann",center=True,pad_mode="reflect",power=2.0,fmin=0.0,fmax=None,**kwargs):"""计算音频梅尔频谱图(Mel Spectrogram):param y: 音频时间序列:param sr: 采样率:param n_mels: number of Mel bands to generate产生的梅尔带数:param n_fft:  length of the FFT window FFT窗口的长度:param hop_length: number of samples between successive frames 帧移(相邻窗之间的距离):param win_length: 窗口的长度为win_length，默认win_length = n_fft:param window::param center: 如果为True，则填充信号y，以使帧 t以y [t * hop_length]为中心。如果为False，则帧t从y [t * hop_length]开始:param pad_mode::param power: 幅度谱的指数。例如1代表能量，2代表功率，等等:param fmin: 最低频率（Hz）:param fmax: 最高频率(以Hz为单位),如果为None,则使用fmax = sr / 2.0:param kwargs::return: 返回Mel频谱shape=(n_mels,n_frames),n_mels是Mel频率的维度(频域),n_frames为时间帧长度(时域)"""mel = librosa.feature.melspectrogram(y=y,sr=sr,S=None,n_mels=n_mels,n_fft=n_fft,hop_length=hop_length,win_length=win_length,window=window,center=center,pad_mode=pad_mode,power=power,fmin=fmin,fmax=fmax,**kwargs)return mel

根据Python版本的librosa.feature.melspectrogram()，项目实现了C++版本melspectrogram

/**** compute mel spectrogram similar with librosa.feature.melspectrogram* @param x      input audio signal* @param sr     sample rate of 'x'* @param n_fft  length of the FFT size* @param n_hop  number of samples between successive frames* @param win    window function. currently only supports 'hann'* @param center same as librosa* @param mode   pad mode. support "reflect","symmetric","edge"* @param power  exponent for the magnitude melspectrogram* @param n_mels number of mel bands* @param fmin   lowest frequency (in Hz)* @param fmax    highest frequency (in Hz)* @return   mel spectrogram matrix*/
static std::vector <std::vector<float>> melspectrogram(std::vector<float> &x, int sr,int n_fft, int n_hop, const std::string &win, bool center,const std::string &mode,float power, int n_mels, int fmin, int fmax)

测试和对比Python和C++版本melspectrogram，二者的返回数值差异已经很小，其可视化的梅尔频谱图基本一致。

版本	数值对比
C++版本
Python版本

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(3) 对齐MFCC

Python版可使用librosa库的librosa.feature.mfcc实现MFCC(Mel-frequency cepstral coefficients)

def librosa_feature_mfcc(y,sr=16000,n_mfcc=128,n_mels=128,n_fft=2048,hop_length=256,win_length=None,window="hann",center=True,pad_mode="reflect",power=2.0,fmin=0.0,fmax=None,dct_type=2,**kwargs):"""计算音频MFCC:param y: 音频时间序列:param sr: 采样率:param n_mfcc: number of MFCCs to return:param n_mels: number of Mel bands to generate产生的梅尔带数:param n_fft:  length of the FFT window FFT窗口的长度:param hop_length: number of samples between successive frames 帧移(相邻窗之间的距离):param win_length: 窗口的长度为win_length，默认win_length = n_fft:param window::param center: 如果为True，则填充信号y，以使帧 t以y [t * hop_length]为中心。如果为False，则帧t从y [t * hop_length]开始:param pad_mode::param power: 幅度谱的指数。例如1代表能量，2代表功率，等等:param fmin: 最低频率（Hz）:param fmax: 最高频率(以Hz为单位),如果为None,则使用fmax = sr / 2.0:param kwargs::return: 返回MFCC shape=(n_mfcc,n_frames)"""# MFCC 梅尔频率倒谱系数mfcc = librosa.feature.mfcc(y=y,sr=sr,S=None,n_mfcc=n_mfcc,n_mels=n_mels,n_fft=n_fft,hop_length=hop_length,win_length=win_length,window=window,center=center,pad_mode=pad_mode,power=power,fmin=fmin,fmax=fmax,dct_type=dct_type,**kwargs)return mfcc

根据Python版本的librosa.feature.mfcc()，项目实现了C++版本MFCC 

/**** compute mfcc similar with librosa.feature.mfcc* @param x      input audio signal* @param sr     sample rate of 'x'* @param n_fft  length of the FFT size* @param n_hop  number of samples between successive frames* @param win    window function. currently only supports 'hann'* @param center same as librosa* @param mode   pad mode. support "reflect","symmetric","edge"* @param power  exponent for the magnitude melspectrogram* @param n_mels number of mel bands* @param fmin   lowest frequency (in Hz)* @param fmax   highest frequency (in Hz)* @param n_mfcc number of mfccs* @param norm   ortho-normal dct basis* @param type   dct type. currently only supports 'type-II'* @return mfcc matrix*/
static std::vector<std::vector<float>> mfcc(std::vector<float> &x, int sr,int n_fft, int n_hop, const std::string &win, bool center, const std::string &mode,float power, int n_mels, int fmin, int fmax,int n_mfcc, bool norm, int type)

测试和对比Python和C++版本MFCC，二者的返回数值差异已经很小，其可视化的MFCC图基本一致。 

版本	数值对比
C++版本	​​​
Python版本

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4.Demo运行

• C++版本，可在项目根目录，终端输入：bash build.sh ，即可运行测试demo

#!/usr/bin/env bash
if [ ! -d "build/" ];thenmkdir "build"
elseecho "exist build"
fi
cd build
cmake ..
make -j4
sleep 1./main

main函数

/*****   @Author : [e-mail]*   @E-mail :*   @Date   :*   @Brief  : C/C++实现Melspectrogram和MFCC*/
#include <iostream>
#include <vector>
#include <algorithm>
#include "librosa/audio_utils.h"
#include "librosa/librosa.h"
#include "librosa/cv_utils.h"using namespace std;int main() {int sr = -1;int n_fft = 400;int hop_length = 160;int n_mel = 64;int fmin = 80;int fmax = 7600;int n_mfcc = 64;int dct_type = 2;float power = 2.f;bool center = false;bool norm = true;string window = "hann";string pad_mode = "reflect";//string audio_file = "../data/data_d2.wav";string audio_file = "../data/data_s1.wav";vector<float> data;int res = read_audio(audio_file.c_str(), data, &sr, false);if (res < 0) {printf("read wav file error: %s\n", audio_file.c_str());return -1;}printf("n_fft      = %d\n", n_fft);printf("n_mel      = %d\n", n_mel);printf("hop_length = %d\n", hop_length);printf("fmin, fmax = (%d,%d)\n", fmin, fmax);printf("sr         = %d, data size=%d\n", sr, data.size());//print_vector("audio data", data);// compute mel Melspectrogramvector<vector<float>> mels_feature = librosa::Feature::melspectrogram(data, sr, n_fft, hop_length, window,center, pad_mode, power, n_mel, fmin, fmax);int mels_w = (int) mels_feature.size();int mels_h = (int) mels_feature[0].size();cv::Mat mels_image = vector2mat<float>(get_vector(mels_feature), 1, mels_h);print_feature("mels_feature", mels_feature);printf("mels_feature size(n_frames,n_mels)=(%d,%d)\n", mels_w, mels_h);image_show("mels_feature(C++)", mels_image, 10);// compute MFCCvector<vector<float>> mfcc_feature = librosa::Feature::mfcc(data, sr, n_fft, hop_length, window, center, pad_mode,power, n_mel, fmin, fmax, n_mfcc, norm, dct_type);int mfcc_w = (int) mfcc_feature.size();int mfcc_h = (int) mfcc_feature[0].size();cv::Mat mfcc_image = vector2mat<float>(get_vector(mfcc_feature), 1, mfcc_h);print_feature("mfcc_feature", mfcc_feature);printf("mfcc_feature size(n_frames,n_mfcc)=(%d,%d)\n", mfcc_w, mfcc_h);image_show("mfcc_feature(C++)", mfcc_image, 10);cv::waitKey(0);printf("finish...");return 0;
}

• Python版本，可在项目根目录，终端输入：python main.py ，即可运行测试demo

# -*-coding: utf-8 -*-
"""@Author :@E-mail : @Date   : 2023-08-01 22:27:56@Brief  :
"""
import cv2
import numpy as np
import librosadef cv_show_image(title, image, use_rgb=False, delay=0):"""调用OpenCV显示图片:param title: 图像标题:param image: 输入是否是RGB图像:param use_rgb: True:输入image是RGB的图像, False:返输入image是BGR格式的图像:param delay: delay=0表示暂停，delay>0表示延时delay毫米:return:"""img = image.copy()if img.shape[-1] == 3 and use_rgb:img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)  # 将BGR转为RGB# cv2.namedWindow(title, flags=cv2.WINDOW_AUTOSIZE)cv2.namedWindow(title, flags=cv2.WINDOW_NORMAL)cv2.imshow(title, img)cv2.waitKey(delay)return imgdef librosa_feature_melspectrogram(y,sr=16000,n_mels=128,n_fft=2048,hop_length=256,win_length=None,window="hann",center=True,pad_mode="reflect",power=2.0,fmin=0.0,fmax=None,**kwargs):"""计算音频梅尔频谱图(Mel Spectrogram):param y: 音频时间序列:param sr: 采样率:param n_mels: number of Mel bands to generate产生的梅尔带数:param n_fft:  length of the FFT window FFT窗口的长度:param hop_length: number of samples between successive frames 帧移(相邻窗之间的距离):param win_length: 窗口的长度为win_length，默认win_length = n_fft:param window::param center: 如果为True，则填充信号y，以使帧 t以y [t * hop_length]为中心。如果为False，则帧t从y [t * hop_length]开始:param pad_mode::param power: 幅度谱的指数。例如1代表能量，2代表功率，等等:param fmin: 最低频率（Hz）:param fmax: 最高频率(以Hz为单位),如果为None,则使用fmax = sr / 2.0:param kwargs::return: 返回Mel频谱shape=(n_mels,n_frames),n_mels是Mel频率的维度(频域),n_frames为时间帧长度(时域)"""mel = librosa.feature.melspectrogram(y=y,sr=sr,S=None,n_mels=n_mels,n_fft=n_fft,hop_length=hop_length,win_length=win_length,window=window,center=center,pad_mode=pad_mode,power=power,fmin=fmin,fmax=fmax,**kwargs)return meldef librosa_feature_mfcc(y,sr=16000,n_mfcc=128,n_mels=128,n_fft=2048,hop_length=256,win_length=None,window="hann",center=True,pad_mode="reflect",power=2.0,fmin=0.0,fmax=None,dct_type=2,**kwargs):"""计算音频MFCC:param y: 音频时间序列:param sr: 采样率:param n_mfcc: number of MFCCs to return:param n_mels: number of Mel bands to generate产生的梅尔带数:param n_fft:  length of the FFT window FFT窗口的长度:param hop_length: number of samples between successive frames 帧移(相邻窗之间的距离):param win_length: 窗口的长度为win_length，默认win_length = n_fft:param window::param center: 如果为True，则填充信号y，以使帧 t以y [t * hop_length]为中心。如果为False，则帧t从y [t * hop_length]开始:param pad_mode::param power: 幅度谱的指数。例如1代表能量，2代表功率，等等:param fmin: 最低频率（Hz）:param fmax: 最高频率(以Hz为单位),如果为None,则使用fmax = sr / 2.0:param kwargs::return: 返回MFCC shape=(n_mfcc,n_frames)"""# MFCC 梅尔频率倒谱系数mfcc = librosa.feature.mfcc(y=y,sr=sr,S=None,n_mfcc=n_mfcc,n_mels=n_mels,n_fft=n_fft,hop_length=hop_length,win_length=win_length,window=window,center=center,pad_mode=pad_mode,power=power,fmin=fmin,fmax=fmax,dct_type=dct_type,**kwargs)return mfccdef read_audio(audio_file, sr=16000, mono=True):"""默认将多声道音频文件转换为单声道，并返回一维数组；如果你需要处理多声道音频文件，可以使用 mono=False,参数来保留所有声道，并返回二维数组。:param audio_file::param sr: sampling rate:param mono: 设置为true是单通道，否则是双通道:return:"""audio_data, sr = librosa.load(audio_file, sr=sr, mono=mono)audio_data = audio_data.T.reshape(-1)return audio_data, srdef print_feature(name, feature):h, w = feature.shape[:2]np.set_printoptions(precision=7, suppress=True, linewidth=(11 + 3) * w)print("------------------------{}------------------------".format(name))for i in range(w):v = feature[:, i].reshape(-1)print("data[{:0=3d},:]={}".format(i, v))def print_vector(name, data):np.set_printoptions(precision=7, suppress=False)print("------------------------%s------------------------\n" % name)print("{}".format(data.tolist()))if __name__ == '__main__':sr = Nonen_fft = 400hop_length = 160n_mel = 64fmin = 80fmax = 7600n_mfcc = 64dct_type = 2power = 2.0center = Falsenorm = Truewindow = "hann"pad_mode = "reflect"audio_file = "data/data_s1.wav"data, sr = read_audio(audio_file, sr=sr, mono=False)print("n_fft      = %d" % n_fft)print("n_mel      = %d" % n_mel)print("hop_length = %d" % hop_length)print("fmin, fmax = (%d,%d)" % (fmin, fmax))print("sr         = %d, data size=%d" % (sr, len(data)))# print_vector("audio data", data)mels_feature = librosa_feature_melspectrogram(y=data,sr=sr,n_mels=n_mel,n_fft=n_fft,hop_length=hop_length,win_length=None,fmin=fmin,fmax=fmax,window=window,center=center,pad_mode=pad_mode,power=power)print_feature("mels_feature", mels_feature)print("mels_feature size(n_frames,n_mels)=({},{})".format(mels_feature.shape[1], mels_feature.shape[0]))cv_show_image("mels_feature(Python)", mels_feature, delay=10)mfcc_feature = librosa_feature_mfcc(y=data,sr=sr,n_mfcc=n_mfcc,n_mels=n_mel,n_fft=n_fft,hop_length=hop_length,win_length=None,fmin=fmin,fmax=fmax,window=window,center=center,pad_mode=pad_mode,power=power,dct_type=dct_type)print_feature("mfcc_feature", mfcc_feature)print("mfcc_feature size(n_frames,n_mfcc)=({},{})".format(mfcc_feature.shape[1], mfcc_feature.shape[0]))cv_show_image("mfcc_feature(Python)", mfcc_feature, delay=10)cv2.waitKey(0)

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5.librosa库C++源码下载

C/C++实现librosa音频处理库melspectrogram和mfcc项目代码下载地址：C/C++实现librosa音频处理库melspectrogram和mfcc

项目源码内容包含：

1. 提供C++版的read_audio()函数读取音频文件,目前仅支持wav格式文件，支持单/双声道音频读取
2. 提供C++版的librosa::Feature::melspectrogram(),实现melspectrogram功能
3. 提供C++版的librosa::Feature::mfcc()，实现MFCC功能
4. 提供OpenCV图谱显示方式
5. 项目demo自带测试数据，编译build完成后，即可运行