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RawBoost.py
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RawBoost.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
from scipy import signal
import copy
"""
___author__ = "Massimiliano Todisco, Hemlata Tak"
__email__ = "{todisco,tak}@eurecom.fr"
"""
'''
Hemlata Tak, Madhu Kamble, Jose Patino, Massimiliano Todisco, Nicholas Evans.
RawBoost: A Raw Data Boosting and Augmentation Method applied to Automatic Speaker Verification Anti-Spoofing.
In Proc. ICASSP 2022, pp:6382--6386.
'''
def randRange(x1, x2, integer):
y = np.random.uniform(low=x1, high=x2, size=(1,))
if integer:
y = int(y)
return y
def normWav(x,always):
if always:
x = x/np.amax(abs(x))
elif np.amax(abs(x)) > 1:
x = x/np.amax(abs(x))
return x
def genNotchCoeffs(nBands,minF,maxF,minBW,maxBW,minCoeff,maxCoeff,minG,maxG,fs):
b = 1
for i in range(0, nBands):
fc = randRange(minF,maxF,0);
bw = randRange(minBW,maxBW,0);
c = randRange(minCoeff,maxCoeff,1);
if c/2 == int(c/2):
c = c + 1
f1 = fc - bw/2
f2 = fc + bw/2
if f1 <= 0:
f1 = 1/1000
if f2 >= fs/2:
f2 = fs/2-1/1000
b = np.convolve(signal.firwin(c, [float(f1), float(f2)], window='hamming', fs=fs),b)
G = randRange(minG,maxG,0);
_, h = signal.freqz(b, 1, fs=fs)
b = pow(10, G/20)*b/np.amax(abs(h))
return b
def filterFIR(x,b):
N = b.shape[0] + 1
xpad = np.pad(x, (0, N), 'constant')
y = signal.lfilter(b, 1, xpad)
y = y[int(N/2):int(y.shape[0]-N/2)]
return y
# Linear and non-linear convolutive noise
def LnL_convolutive_noise(x,N_f,nBands,minF,maxF,minBW,maxBW,minCoeff,maxCoeff,minG,maxG,minBiasLinNonLin,maxBiasLinNonLin,fs):
y = [0] * x.shape[0]
for i in range(0, N_f):
if i == 1:
minG = minG-minBiasLinNonLin;
maxG = maxG-maxBiasLinNonLin;
b = genNotchCoeffs(nBands,minF,maxF,minBW,maxBW,minCoeff,maxCoeff,minG,maxG,fs)
y = y + filterFIR(np.power(x, (i+1)), b)
y = y - np.mean(y)
y = normWav(y,0)
return y
# Impulsive signal dependent noise
def ISD_additive_noise(x, P, g_sd):
beta = randRange(0, P, 0)
y = copy.deepcopy(x)
x_len = x.shape[0]
n = int(x_len*(beta/100))
p = np.random.permutation(x_len)[:n]
f_r= np.multiply(((2*np.random.rand(p.shape[0]))-1),((2*np.random.rand(p.shape[0]))-1))
r = g_sd * x[p] * f_r
y[p] = x[p] + r
y = normWav(y,0)
return y
# Stationary signal independent noise
def SSI_additive_noise(x,SNRmin,SNRmax,nBands,minF,maxF,minBW,maxBW,minCoeff,maxCoeff,minG,maxG,fs):
noise = np.random.normal(0, 1, x.shape[0])
b = genNotchCoeffs(nBands,minF,maxF,minBW,maxBW,minCoeff,maxCoeff,minG,maxG,fs)
noise = filterFIR(noise, b)
noise = normWav(noise,1)
SNR = randRange(SNRmin, SNRmax, 0)
noise = noise / np.linalg.norm(noise,2) * np.linalg.norm(x,2) / 10.0**(0.05 * SNR)
x = x + noise
return x
def process_Rawboost_feature(feature, sr,args,algo):
# Data process by Convolutive noise (1st algo)
if algo==1:
feature =LnL_convolutive_noise(feature,args.N_f,args.nBands,args.minF,args.maxF,args.minBW,args.maxBW,args.minCoeff,args.maxCoeff,args.minG,args.maxG,args.minBiasLinNonLin,args.maxBiasLinNonLin,sr)
# Data process by Impulsive noise (2nd algo)
elif algo==2:
feature=ISD_additive_noise(feature, args.P, args.g_sd)
# Data process by coloured additive noise (3rd algo)
elif algo==3:
feature=SSI_additive_noise(feature,args.SNRmin,args.SNRmax,args.nBands,args.minF,args.maxF,args.minBW,args.maxBW,args.minCoeff,args.maxCoeff,args.minG,args.maxG,sr)
# Data process by all 3 algo. together in series (1+2+3)
elif algo==4:
feature =LnL_convolutive_noise(feature,args.N_f,args.nBands,args.minF,args.maxF,args.minBW,args.maxBW,
args.minCoeff,args.maxCoeff,args.minG,args.maxG,args.minBiasLinNonLin,args.maxBiasLinNonLin,sr)
feature=ISD_additive_noise(feature, args.P, args.g_sd)
feature=SSI_additive_noise(feature,args.SNRmin,args.SNRmax,args.nBands,args.minF,
args.maxF,args.minBW,args.maxBW,args.minCoeff,args.maxCoeff,args.minG,args.maxG,sr)
# Data process by 1st two algo. together in series (1+2)
elif algo==5:
feature =LnL_convolutive_noise(feature,args.N_f,args.nBands,args.minF,args.maxF,args.minBW,args.maxBW,
args.minCoeff,args.maxCoeff,args.minG,args.maxG,args.minBiasLinNonLin,args.maxBiasLinNonLin,sr)
feature=ISD_additive_noise(feature, args.P, args.g_sd)
# Data process by 1st and 3rd algo. together in series (1+3)
elif algo==6:
feature =LnL_convolutive_noise(feature,args.N_f,args.nBands,args.minF,args.maxF,args.minBW,args.maxBW,
args.minCoeff,args.maxCoeff,args.minG,args.maxG,args.minBiasLinNonLin,args.maxBiasLinNonLin,sr)
feature=SSI_additive_noise(feature,args.SNRmin,args.SNRmax,args.nBands,args.minF,args.maxF,args.minBW,args.maxBW,args.minCoeff,args.maxCoeff,args.minG,args.maxG,sr)
# Data process by 2nd and 3rd algo. together in series (2+3)
elif algo==7:
feature=ISD_additive_noise(feature, args.P, args.g_sd)
feature=SSI_additive_noise(feature,args.SNRmin,args.SNRmax,args.nBands,args.minF,args.maxF,args.minBW,args.maxBW,args.minCoeff,args.maxCoeff,args.minG,args.maxG,sr)
# Data process by 1st two algo. together in Parallel (1||2)
elif algo==8:
feature1 =LnL_convolutive_noise(feature,args.N_f,args.nBands,args.minF,args.maxF,args.minBW,args.maxBW,
args.minCoeff,args.maxCoeff,args.minG,args.maxG,args.minBiasLinNonLin,args.maxBiasLinNonLin,sr)
feature2=ISD_additive_noise(feature, args.P, args.g_sd)
feature_para=feature1+feature2
feature=normWav(feature_para,0) #normalized resultant waveform
# original data without Rawboost processing
else:
feature=feature
return feature