c/c++语言开发共享语音预处理（相同话语不同人的对比）

显示同一段语音，不同说话人对比下效果
波形图

import wave import matplotlib.pyplot as plt import numpy as np from scipy.io import wavfile """读取双通道波形并绘制波形图""" f = wavfile.read("./test1.wav") nframes = len(f[1])  #获取采样点数 nchannels = 1  #  通道数=1 framerate = f[0] #  获取采样频率 wave_data = f[1]  #  获取音频数据 wave_data = np.reshape(wave_data, [nframes, nchannels]) # 最后通过采样点数和取样频率计算出每个取样的时间 time = np.arange(0, nframes) * (1.0 / framerate)  plt.figure() plt.subplot(3, 1, 1) plt.plot(time, wave_data[:, 0]) plt.xlabel("time (seconds)") plt.ylabel("Amplitude") plt.title("people_one") plt.grid()  # 标尺  plt.subplot(3, 1, 3) f = wavfile.read("./test2.wav") nframes = len(f[1])  #获取采样点数 nchannels = 1  #  通道数=1 framerate = f[0] #  获取采样频率 wave_data = f[1]  #  获取音频数据 wave_data = np.reshape(wave_data, [nframes, nchannels]) time = np.arange(0, nframes) * (1.0 / framerate) plt.plot(time, wave_data[:, 0], c="g") plt.xlabel("time (seconds)") plt.ylabel("Amplitude")  plt.title("people_two") plt.grid() plt.show()  

从时域转化到频域
看看效果是什么

 import numpy as np from scipy.io import wavfile import matplotlib.pyplot as plt  def fft(path):     sampling_freq, audio = wavfile.read(path)  # 读取文件      audio = audio / np.max(audio)  # 归一化，标准化      # 应用傅里叶变换     fft_signal = np.fft.fft(audio)      fft_signal = abs(fft_signal)      # 建立时间轴     Freq = np.arange(0, len(fft_signal))     return Freq,fft_signal  plt.figure() plt.subplot(3, 1, 1) test1_freq , test1_signal = fft("./test1.wav") plt.plot(test1_freq, test1_signal, color='blue') plt.xlabel('Freq (in kHz)') plt.ylabel('Amplitude') plt.title("people_one") plt.grid()  # 标尺  plt.subplot(3, 1, 3) f = wavfile.read("./test2.wav") test2_freq , test2_signal = fft("./test2.wav") plt.plot(test2_freq, test2_signal, color='g') plt.xlabel('Freq (in kHz)') plt.ylabel('Amplitude') plt.title("people_two") plt.grid()  plt.show()   

生成语谱图

import numpy as np from scipy.io import wavfile import matplotlib.pyplot as plt from python_speech_features import mfcc, logfbank import wave def test(path):     f = wave.open(path, 'rb')     params = f.getparams()     nchannels, sampwidth, framerate, nframes = params[:4]     strdata = f.readframes(nframes)     wavedata = np.fromstring(strdata, dtype=np.int16)     wavedata = wavedata * 1.0 / (max(abs(wavedata)))     wavedata = np.reshape(wavedata, [nframes, nchannels]).T     f.close()     return wavedata,framerate  plt.figure() plt.subplot(3, 1, 1) wave_1, framerate_1 = test('test1.wav') plt.specgram(wave_1[0], Fs=framerate_1, scale_by_freq=True, sides = 'default') plt.ylabel('Frequency(HZ)') plt.xlabel('Time(s)') plt.grid()  # 标尺  plt.subplot(3, 1, 3) wave_2, framerate_2 = test('test2.wav') plt.specgram(wave_2[0], Fs=framerate_2, scale_by_freq=True, sides = 'default') plt.ylabel('Frequency(HZ)') plt.xlabel('Time(s)') plt.grid()  plt.show()  

提取mfcc特征图

 from scipy.io import wavfile import matplotlib.pyplot as plt from python_speech_features import mfcc, logfbank  def test(path):     # 读取输入音频文件     sampling_freq, audio = wavfile.read(path)     # 提取MFCC特征     mfcc_features = mfcc(audio, sampling_freq)     mfcc_features = mfcc_features.T     return  mfcc_features  # plt.figure() # plt.subplot(3, 1, 1) mfcc_1 = test('test1.wav') plt.matshow(mfcc_1) plt.xlabel('MFCC_windows  '+ str(mfcc_1.shape[0]) + '        ' +'MFCC_features_length  ' + str(mfcc_1.shape[1])) plt.grid()  # 标尺  # plt.subplot(3, 1, 3) mfcc_2 = test('test2.wav') plt.matshow(mfcc_2) plt.xlabel('MFCC_windows  '+ str(mfcc_2.shape[0]) + '        ' +'MFCC_features_length  ' + str(mfcc_2.shape[1])) plt.grid()  # 标尺  plt.show()   

c/c++开发分享语音预处理（相同话语不同人的对比）地址：https://blog.csdn.net/qq_40703471/article/details/109860777