Python谱减法语音降噪实例谱减法语音降噪实例
代码中用到了nextpow2，其中n = nextpow2(x) 表示最接近x的2的n次幂。
#!/usr/bin/env python
import numpy as np
import wave
import nextpow2
import math
# 打开WAV文档
f = wave.open("filename.wav")
# 读取格式信息
# (nchannels, sampwidth, framerate, nframes, comptype, compname)
params = f.getparams()
nchannels, sampwidth, framerate, nframes = params[:4] fs = framerate
# 读取波形数据
str_data = f.readframes(nframes)
f.close()
# 将波形数据转换为数组
x = np.fromstring(str_data, dtype=np.short)
# 计算参数
len_ = 20 * fs // 1000
PERC = 50
len1 = len_ * PERC // 100
len2 = len_ - len1
# 设置默认参数
Thres = 3
Expnt = 2.0
beta = 0.002
G = 0.9
# 初始化汉明窗
win = np.hamming(len_)
# normalization gain for overlap+add with 50% overlap
winGain = len2 / sum(win)
# Noise magnitude calculations - assuming that the first 5 frames is noise/silence
nFFT = 2 * 2 ** (nextpow2.nextpow2(len_))
noise_mean = np.zeros(nFFT)
j = 0
for k in range(1, 6):
noise_mean = noise_mean + abs(np.fft.fft(win * x[j:j + len_], nFFT))
j = j + len_
noise_mu = noise_mean / 5
# --- allocate memory and initialize various variables
k = 1
img = 1j
x_old = np.zeros(len1)
Nframes = len(x) // len2 - 1
xfinal = np.zeros(Nframes * len2)
# ========================= Start Processing ===============================
for n in range(0, Nframes):
# Windowing
insign = win * x[k-1:k + len_ - 1] # compute fourier transform of a frame
spec = np.fft.fft(insign, nFFT)
# compute the magnitude
sig = abs(spec)
# save the noisy phase information
theta = np.angle(spec)
SNRseg = 10 * np.log10(np.linalg.norm(sig, 2) ** 2 / np.linalg.norm(noise_mu, 2) ** 2)
def berouti(SNR):
if -5.0 <= SNR <= 20.0:
a = 4 - SNR * 3 / 20
else:
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