最近在看speech enhancement 内容,看完谱减法部分后,在网上找相应的代码来看,然后将MATLAB代码转成Python代码,顺便学习一下Python的使用。
论文《Enhancement of speech corrupted by acoustic noise》提出的实现:
算法流程如下:
效果如下:
这是一段火车站附近的录音,噪声比较平稳;设置的VAD阈值是3dB,一般应用上设置的是6dB
虽然floor值的存在填充了频谱中一定的 “波谷”, 但可以看到还是有一些音乐噪声的存在。
Python代码如下:
#!/usr/bin/env python
# encoding: utf-8
'''
@author: Kolor
@license:
@contact: colorsu1922@163.com
@software: pycharm
@file: spec_sub.py
@time: 2020/12/21 23:44
@desc:
Implements the basic power spectral subtraction algorithm [1].
Usage: specsub(noisyFile, outputFile)
noisyFile - noisy speech file in .wav format
outputFile - enhanced output file in .wav format
Algorithm uses the first 5 frames to estimate the noise psd, and
then uses a very simple VAD algorithm for updating the noise psd.
Alternatively, the VAD in the file ss_rdc.m (this folder) can be used.
References:
[1] Berouti, M., Schwartz, M., and Makhoul, J. (1979). Enhancement of speech
corrupted by acoustic noise. Proc. IEEE Int. Conf. Acoust., Speech,
Signal Processing, 208-211.
Author: Philipos C. Loizou
Copyright (c) 2006 by Philipos C. Loizou
$Revision: 0.0 $ $Date: 10/09/2006 $
-------------------------------------------------------------------------
'''
from typing import Type
import librosa
import numpy as np
import sys
import matplotlib.pyplot as plt
import soundfile as sf
def rh_spec_sub(infile, outfile):
print(np.__version__)
x, samp_rate = librosa.load(infile, sr=8000, mono=True)
# sys.exit("pause")
# =============== Initialize variables ===============
# Frame size in samples
frame_len = int(np.floor(20 * samp_rate / 1000))
if np.remainder(frame_len, 2) == 1:
frame_len = frame_len + 1
# window overlap in percent of frame size
overlap_percent = 50
len1 = int(np.floor(frame_len * overlap_percent / 100))
len2 = int(frame_len - len1)
# VAD threshold in dB SNR
vad_thresh = 3
# power exponent
alpha = 2.0
FLOOR = 0.002
G = 0.9
# define window
win = np.hanning(frame_len+1)[0:frame_len]
win_gain = len2 / np.sum(win) # normalization gain for overlap+add with 50% overlap
# Noise magnitude calculations - assuming that the first 5 frames is noise/silence
NFFT = int(2 * 2 ** next_pow2(frame_len))
noise_mean = np.zeros(NFFT)
j = 0
for k in range(0, 5):
windowed = np.multiply(win, x[j:j+frame_len])
noise_mean = np.add(noise_mean, np.abs(np.fft.fft(windowed, NFFT)))
j = j + frame_len
noise_mu = noise_mean / 5
# --- allocate memory and initialize various variables
k = 0
img = 1j # np.sqrt(-1)
x_old = np.zeros(len1)
Nframs = int(np.floor(len(x)/len2) - 1)
xfinal = np.zeros(Nframs * len2)
# =============================== Start Processing ==================================
for n in range(0, Nframs):
insign = np.multiply(win, x[k:k+frame_len])
spec = np.fft.fft(insign, NFFT)
sig = np.abs(spec) # compute the magnitude
# save the noisy phase information
theta = np.angle(spec)
SNR_seg = 10*np.log10(np.linalg.norm(sig, 2)**2 / np.linalg.norm(noise_mu, 2)**2)
if alpha == 1.0:
beta = berouti1(SNR_seg)
else:
beta = berouti(SNR_seg)
# &&&&&&&&&&&&&&&
sub_speech = sig ** alpha - beta * noise_mu ** alpha
diffw = sub_speech - FLOOR * noise_mu ** alpha
# floor negative component
z = np.argwhere(diffw < 0)
if len(z) is not 0:
sub_speech[z] = FLOOR * noise_mu[z] ** alpha
# --- implement a simple VAD detector - -------------
if SNR_seg < vad_thresh: # Update noise spectrum
noise_temp = G * noise_mu ** alpha + (1 - G) * sig ** alpha
noise_mu = noise_temp ** (1 / alpha) # new noise spectrum
# to ensure conjugate symmetry for real reconstruction
sub_speech[int(NFFT/2) + 1: NFFT] = np.flipud(sub_speech[1: int(NFFT/2)])
x_phase = (sub_speech**(1/alpha)) * (np.cos(theta) + img * (np.sin(theta)))
# take the iFFT
xi = np.real(np.fft.ifft(x_phase))
# plt.plot(xi)
# plt.show()
# --- Overlap and add ---------------
xfinal[k:k+len2] = x_old + xi[0:len1]
x_old = xi[len1:frame_len]
k = k + len2
# write output
sf.write(outfile, win_gain * xfinal, samp_rate, 'PCM_16')
def berouti1(snr):
beta = 0
if -5.0 <= snr <= 20:
beta = 3 - snr * 2 / 20
elif snr < -5.0:
beta = 4
elif snr > 20:
beta = 1
return beta
def berouti(snr):
beta = 0
if -5.0 <= snr <= 20:
beta = 4 - snr * 3 / 20
elif snr < -5.0:
beta = 5
elif snr > 20:
beta = 1
return beta
def next_pow2(n):
return np.ceil(np.log2(np.abs(n)))
def load_audio(filename, trace=0):
"""
load wav file using audioread.
This is not available in python x,y.
"""
data = np.array([])
with audioread.audio_open(filename) as af:
trace_n = af.channels
if trace >= trace_n:
print('number of traces in file is', trace_n)
quit()
nsamp = np.ceil(af.samplerate * af.duration)
print(f"nsamp =%d" % nsamp)
data = np.ascontiguousarray(np.zeros(nsamp, 1))
index = 0
for buffer in af:
full_data = np.fromstring(buffer).reshape(-1, af.channels)
n = full_data.shape[0]
if index + n > len(data):
n = len(data) - index
if n > 0:
data[index:index + n] = full_data[:n, trace]
index += n
else:
break
return af.samplerate, data / 2.0 ** 15, 'a.u.'
上面代码是根据speech enhancement中的MATLAB代码改写的,仅用作学习用途。
原始的MATLAB代码如下:
function specsub(filename,outfile)
% Implements the basic power spectral subtraction algorithm [1].
%
% Usage: specsub(noisyFile, outputFile)
%
% noisyFile - noisy speech file in .wav format
% outputFile - enhanced output file in .wav format
%
% Algorithm uses the first 5 frames to estimate the noise psd, and
% then uses a very simple VAD algorithm for updating the noise psd.
% Alternatively, the VAD in the file ss_rdc.m (this folder) can be used.
%
% References:
% [1] Berouti, M., Schwartz, M., and Makhoul, J. (1979). Enhancement of speech
% corrupted by acoustic noise. Proc. IEEE Int. Conf. Acoust., Speech,
% Signal Processing, 208-211.
%
% Author: Philipos C. Loizou
%
% Copyright (c) 2006 by Philipos C. Loizou
% $Revision: 0.0 $ $Date: 10/09/2006 $
%-------------------------------------------------------------------------
if nargin<2
fprintf('Usage: specsub noisyfile.wav outFile.wav \n\n');
return;
end
[x,Srate]=audioread(filename);
% =============== Initialize variables ===============
%
len=floor(20*Srate/1000); % Frame size in samples
if rem(len,2)==1, len=len+1; end;
PERC=50; % window overlap in percent of frame size
len1=floor(len*PERC/100);
len2=len-len1;
Thres=3; % VAD threshold in dB SNRseg
alpha=2.0; % power exponent
FLOOR=0.002;
G=0.9;
win=hanning(len, 'periodic'); %tukey(len,PERC); % define window
winGain=len2/sum(win); % normalization gain for overlap+add with 50% overlap
% Noise magnitude calculations - assuming that the first 5 frames is noise/silence
%
nFFT=2*2^nextpow2(len);
noise_mean=zeros(nFFT,1);
j=1;
for k=1:5
windowed = win.*x(j:j+len-1);
noise_mean=noise_mean+abs(fft(win.*x(j:j+len-1),nFFT));
j=j+len;
end
noise_mu=noise_mean/5;
%--- allocate memory and initialize various variables
k=1;
img=sqrt(-1);
x_old=zeros(len1,1);
Nframes=floor(length(x)/len2)-1;
xfinal=zeros(Nframes*len2,1);
%=============================== Start Processing ==================================
%
for n=1:Nframes
t = x(k:k+len - 1);
insign=win.*x(k:k+len-1); %Windowing
spec=fft(insign,nFFT); %compute fourier transform of a frame
sig=abs(spec); % compute the magnitude
%save the noisy phase information
theta=angle(spec);
SNRseg=10*log10(norm(sig,2)^2/norm(noise_mu,2)^2);
if alpha==1.0
beta=berouti1(SNRseg);
else
beta=berouti(SNRseg);
end
%&&&&&&&&&
sub_speech=sig.^alpha - beta*noise_mu.^alpha;
diffw = sub_speech-FLOOR*noise_mu.^alpha;
% Floor negative components
z=find(diffw <0);
if~isempty(z)
sub_speech(z)=FLOOR*noise_mu(z).^alpha;
end
% --- implement a simple VAD detector --------------
%
if (SNRseg < Thres) % Update noise spectrum
noise_temp = G*noise_mu.^alpha+(1-G)*sig.^alpha;
noise_mu=noise_temp.^(1/alpha); % new noise spectrum
end
sub_speech(nFFT/2+2:nFFT)=flipud(sub_speech(2:nFFT/2)); % to ensure conjugate symmetry for real reconstruction
x_phase=(sub_speech.^(1/alpha)).*(cos(theta)+img*(sin(theta)));
% take the IFFT
xi=real(ifft(x_phase));
% --- Overlap and add ---------------
xfinal(k:k+len2-1)=x_old+xi(1:len1);
x_old=xi(1+len1:len);
k=k+len2;
end
%========================================================================================
% wavwrite(winGain*xfinal,Srate,16,outfile);
audiowrite(outfile, winGain*xfinal,Srate);
%--------------------------------------------------------------------------
function a=berouti1(SNR)
if SNR>=-5.0 & SNR<=20
a=3-SNR*2/20;
else
if SNR<-5.0
a=4;
end
if SNR>20
a=1;
end
end
function a=berouti(SNR)
if SNR>=-5.0 & SNR<=20
a=4-SNR*3/20;
else
if SNR<-5.0
a=5;
end
if SNR>20
a=1;
end
end