代码含有：wav音频读取、语音混合、语谱图
# coding = utf-8 import numpy as np from matplotlib import pyplot as plt import librosa import scipy def SNR(x1, x2): from numpy.linalg import norm return 20 * np.log10(norm(x1) / norm(x2)) def signal_by_db(x1, x2, snr, handle_method): x1 = x1.astype(np.int32) x2 = x2.astype(np.int32) l1 = x1.shape[0] l2 = x2.shape[0] if l1 != l2: if handle_method == 'cut': ll = min(l1, l2) x1 = x1[:ll] x2 = x2[:ll] elif handle_method == 'append': ll = max(l1, l2) print(ll) if l1 < ll: x1 = np.append(x1, x1[:ll-l1]) if l2 < ll: for i in range(int(l1/l2)+5): x2 = np.append(x2, x2[:ll]) ll2 = min(x1.shape[0], x2.shape[0]) x1 = x1[:ll2] x2 = x2[:ll2] from numpy.linalg import norm x2 = x2 / norm(x2) * norm(x1) / (10.0 ** (0.05 * snr)) mix = x1 + x2 return mix if __name__ == '__main__': num_FFT=512 hop_size=128 # sr, speech_data = scipy.io.wavfile.read('./datasets/timit/test/sa1.wav') sr, speech_data = scipy.io.wavfile.read(u"./datasets/timit/test/sa1_n.wav") sr, noise_data = scipy.io.wavfile.read('./datasets/noise/NOISEX/white.wav') # sr, noise_data = scipy.io.wavfile.read('./datasets/noise/NOISEX/babble.wav') plt.figure(figsize=(10, 10)) speech_data = speech_data.astype(float) #将numpy array 有int转换为float S = librosa.stft(speech_data, n_fft=num_FFT, hop_length=hop_size, window='hanning') S=np.log(np.abs(S)**2) plt.subplot(311) plt.imshow(librosa.power_to_db(librosa.feature.melspectrogram(y=speech_data, sr=sr, n_fft=num_FFT, hop_length=hop_size),ref=np.max), cmap="hot") plt.title('Clean Spectrogram') plt.colorbar(format='%+2.0f dB') noisy_speech = signal_by_db(speech_data, noise_data, 15, 'cut') #得到snr分呗的混哈语音 S = librosa.stft(speech_data, n_fft=num_FFT, hop_length=hop_size, window='hanning') S=np.log(np.abs(S)**2) plt.subplot(312) plt.imshow(librosa.power_to_db(librosa.feature.melspectrogram(y=noisy_speech, sr=sr, n_fft=num_FFT, hop_length=hop_size),ref=np.max), cmap="hot") plt.title('Noisy Spectrogram') plt.colorbar(format='%+2.0f dB') noisy_speech = signal_by_db(speech_data, noise_data, 0, 'cut') S = librosa.stft(speech_data, n_fft=num_FFT, hop_length=hop_size, window='hanning') S=np.log(np.abs(S)**2) plt.subplot(313) plt.imshow(librosa.power_to_db(librosa.feature.melspectrogram(y=noisy_speech, sr=sr, n_fft=num_FFT, hop_length=hop_size),ref=np.max), cmap="hot") plt.title('Noisy Spectrogram') plt.colorbar(format='%+2.0f dB') plt.show()
