python中基于谱门的噪声抑制
noisereduce的Python项目详细描述
在python中使用谱门降低噪声
- 该算法基于降噪效果的outlined by Audacity（但不是完全再现）算法（Link to C++ code）
- 该算法需要两个输入：
- anoise包含音频剪辑原始噪声的音频剪辑
- 一个signal音频剪辑，包含要删除的信号和噪声
算法步骤
- 通过噪声音频剪辑计算fft
- 通过对噪声（频率）的fft计算统计值
- 根据噪声的统计信息（以及算法的期望灵敏度）计算阈值
- 在信号上计算fft
- 通过将信号fft与阈值进行比较来确定掩模
- 在频率和时间上使用滤波器平滑掩模
- 掩模被应用于信号的fft，并被反转
安装
pip install noisereduce
noisereduce可以选择使用tensorflow作为后端来加速fft和高斯卷积。它没有列在requirements.txt中，因为（1）它是可选的，（2）tensorflow gpu和tensorflow（cpu）都与这个包兼容。该包要求TensorFlow 2+用于所有TensorFlow操作。
用法
（请参阅笔记本）
import noisereduce as nr
# load data
rate, data = wavfile.read("mywav.wav")
# select section of data that is noise
noisy_part = data[10000:15000]
# perform noise reduction
reduced_noise = nr.reduce_noise(audio_clip=data, noise_clip=noisy_part, verbose=True)
noise_reduce
的参数
n_grad_freq (int): how many frequency channels to smooth over with the mask.
n_grad_time (int): how many time channels to smooth over with the mask.
n_fft (int): number audio of frames between STFT columns.
win_length (int): Each frame of audio is windowed by `window()`. The window will be of length `win_length` and then padded with zeros to match `n_fft`..
hop_length (int):number audio of frames between STFT columns.
n_std_thresh (int): how many standard deviations louder than the mean dB of the noise (at each frequency level) to be considered signal
prop_decrease (float): To what extent should you decrease noise (1 = all, 0 = none)
pad_clipping (bool): Pad the signals with zeros to ensure that the reconstructed data is equal length to the data
use_tensorflow (bool): Use tensorflow as a backend for convolution and fft to speed up computation
verbose (bool): Whether to plot the steps of the algorithm
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