torchvision 0.4，torchaudio 0.3 和 torchtext 0.4。每项工具都进行了新的优化与改进，兼容性更强...
abs(stft) ** 2 # 设置阈值(根据实际情况调整) threshold = np.mean(power) * 0.5 # 将低于阈值的能量谱置为0 mask = power < threshold power[mask] = 0 # 逆短时傅里叶变换(ISTFT)得到降噪后的音频 denoised_stft = np.sqrt(power) * np.exp(1j * np.angle(stft)) denoised_audio = librosa...
0)# ISTFTy_clean = librosa.istft(S_clean)# 输出降噪后的音频librosa.output.write_wav('clean_au...
function x = istft(d, ftsize, w, h) % X = istft(D, F, W, H) Inverse short-time Fourier transform. % Performs overlap-add resynthesis from the short-time Fourier transform % data in D. Each column of D is taken as the result of an F-point % fft; each successive frame was of...
短时傅里叶变换的逆变换调用:“ torch.istft ” torch.istft(input, n_fft, hop_length=None, win_length=None, window=None, center=True, normalized=False, onesided=True, length=None) 1. 下面是函数的一些主要参数及含义: input:输入的 STFT 数据,形状为(batch, channels, freq, time, complex=2)...
y_processed = librosa.istft(stft) 4、保存处理后的音频文件 最后,我们需要将处理后的音频文件保存: import soundfile as sf 保存处理后的音频文件 sf.write("output.wav", y_processed, sr) 三、使用神经网络模型 近年来,深度学习在音频处理领域取得了显著的成果。通过训练神经网络模型,我们可以更准确地去除音频...
[np.newaxis, :]) component_1 = librosa.istft(component_1_mag * np.exp(1j * phase)) # Step 4: Apply a bandpass filter def butter_bandpass(lowcut, highcut, fs, order=5): nyquist = 0.5 * fs low = lowcut / nyquist high = highcut / nyquist b, a = butter(order, [low, high]...
max) # 设置阈值以分离人声 threshold = -50 # 根据实际情况调整 mask = np.where(S_db >= threshold, 1, 0) # 应用掩膜增强人声 D_enhanced = D * mask # 反向计算到时域 y_enhanced = librosa.istft(D_enhanced) 5. 测试并评估降噪和人声增强后的语音质量 我们可以将增强后的语音信号保存为...
为什么stft(istft(x))是≠x? 、、 使用PyTorch,我计算了张量的短时傅立叶逆变换的短时傅立叶变换。我已经这样做了,如下所示,给定一个张量x。对于x,实部和虚部相等,或者虚部设置为零--两者都会产生相同的问题。似乎stft(istft(x))只能接收特定频率的x。x (top) and stft of istft of x (bottom) 我也...
xhat = istft(X, fs, T, hop) # Plot the input and output signals over 0.1 seconds. T1 = int(0.1*fs) pylab.figure() pylab.plot(t[:T1], x[:T1], t[:T1], xhat[:T1]) pylab.xlabel('Time (seconds)') pylab.figure() pylab.plot(t[-T1:], x[-T1:], t[-T1:], xhat[-T1...