import librosaimport matplotlibimport numpy as npimport matplotlibpyplot as pltfrom scipyfft import fftimport librosadisplaypltfiguredpi=600 # 将显示的所有图分辨率调高matplotlibrcfontfamily=SimHei # 显示中文matplotli

import librosa import matplotlib import numpy as np import matplotlib.pyplot as plt from scipy.fft import fft import librosa.display

plt.figure(dpi=600) # 将显示的所有图分辨率调高 matplotlib.rc("font",family='SimHei') # 显示中文 matplotlib.rcParams['axes.unicode_minus']=False # 显示符号

def displayWaveform(sample1, sample2): # 显示语音时域波形 """ display waveform of a given speech sample :param sample_name: speech sample name :param fs: sample frequency :return: """ samples1, sr1 = librosa.load(sample1, sr=16000) samples2, sr2 = librosa.load(sample2, sr=16000) # samples = samples[6000:16000]

print(len(samples1), sr1)
print(len(samples2), sr2)
time1 = np.arange(0, len(samples1)) * (1.0 / sr1)
time2 = np.arange(0, len(samples2)) * (1.0 / sr2)

plt.figure(figsize=(18, 8))
plt.subplot(211)
plt.plot(time1, samples1)
plt.title("语音信号1时域波形")
plt.xlabel("时长（秒）")
plt.ylabel("振幅")
plt.subplot(212)
plt.plot(time2, samples2)
plt.title("语音信号2时域波形")
plt.xlabel("时长（秒）")
plt.ylabel("振幅")
# plt.savefig("your dir\语音信号时域波形图", dpi=600)
plt.show()

def displaySpectrum(sample1, sample2): # 显示语音频域谱线 x1, sr1 = librosa.load(sample1, sr=16000) x2, sr2 = librosa.load(sample2, sr=16000) print(len(x1), len(x2)) # ft = librosa.stft(x) # magnitude = np.abs(ft) # 对fft的结果直接取模（取绝对值），得到幅度magnitude # frequency = np.angle(ft) # (0, 16000, 121632)

ft1 = fft(x1)
ft2 = fft(x2)
magnitude1 = np.absolute(ft1)  # 对fft的结果直接取模（取绝对值），得到幅度magnitude
magnitude2 = np.absolute(ft2)  # 对fft的结果直接取模（取绝对值），得到幅度magnitude
frequency1 = np.linspace(0, sr1, len(magnitude1))  # (0, 16000, 121632)
frequency2 = np.linspace(0, sr2, len(magnitude2))  # (0, 16000, 121632)

print(len(magnitude1), type(magnitude1), np.max(magnitude1), np.min(magnitude1))
print(len(frequency1), type(frequency1), np.max(frequency1), np.min(frequency1))
print(len(magnitude2), type(magnitude2), np.max(magnitude2), np.min(magnitude2))
print(len(frequency2), type(frequency2), np.max(frequency2), np.min(frequency2))

# plot spectrum，限定[:40000]
plt.figure(figsize=(18, 8))
plt.subplot(211)
plt.plot(frequency1[:40000], magnitude1[:40000])  # magnitude spectrum
plt.title("语音信号1频域谱线")
plt.xlabel("频率（赫兹）")
plt.ylabel("幅度")
plt.subplot(212)
plt.plot(frequency2[:40000], magnitude2[:40000])  # magnitude spectrum
plt.title("语音信号2频域谱线")
plt.xlabel("频率（赫兹）")
plt.ylabel("幅度")
#plt.savefig("your dir\语音信号频谱图", dpi=600)
plt.show()

def displaySpectrogram(sample1, sample2): x1, sr1 = librosa.load(sample1, sr=16000) x2, sr2 = librosa.load(sample2, sr=16000)

# compute power spectrogram with stft(short-time fourier transform):
# 基于stft，计算power spectrogram
spectrogram1 = librosa.amplitude_to_db(librosa.stft(x1))
spectrogram2 = librosa.amplitude_to_db(librosa.stft(x2))

# show
plt.figure(figsize=(18, 8))
plt.subplot(211)
librosa.display.specshow(spectrogram1, y_axis='log')
plt.colorbar(format='%+2.0f dB')
plt.title('语音信号1对数谱图')
plt.xlabel('时长（秒）')
plt.ylabel('频率（赫兹）')
plt.subplot(212)
librosa.display.specshow(spectrogram2, y_axis='log')
plt.colorbar(format='%+2.0f dB')
plt.title('语音信号2对数谱图')
plt.xlabel('时长（秒）')
plt.ylabel('频率（赫兹）')
plt.show()

if name == 'main': sample1 = r'enhanced_p232_036.wav' sample2 = r'clean_p232_036.wav' displayWaveform(sample1, sample2) displaySpectrum(sample1, sample2) displaySpectrogram(sample1, sample2