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test_no_howling_detection.py
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test_no_howling_detection.py
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#!/usr/bin/python
from __future__ import division
# import
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt
import soundfile as sf
from pyHowling import plot_notch_filter
def main():
input_file = "test/LDC93S6A.wav"
howling_file = "test/added_howling.wav"
output_file = "test/removed_howling.wav"
#load clean speech file
x, Srate = sf.read(input_file)
#pre design a room impulse response
rir = np.loadtxt('test/path.txt', delimiter='\t')
plt.figure()
plt.plot(rir)
#G : gain from mic to speaker
G = 0.2
# ====== set parameters ========
interval = 0.02 #frame interval = 0.02s
Slen = int(np.floor(interval * Srate))
if Slen % 2 == 1:
Slen = Slen + 1
PERC = 50 #window overlap in percent of frame size
len1 = int(np.floor(Slen * PERC / 100))
len2 = int(Slen - len1)
nFFT = 2 * Slen
plt.figure()
plt.subplot(2,1,1)
plt.plot(x)
plt.xlim(0, len(x))
plt.subplot(2,1,2)
plt.specgram(x, NFFT=nFFT, Fs=Srate, noverlap=len2, cmap='jet')
plt.ylim((0, 5000))
plt.ylabel("Frquency (Hz)")
plt.xlabel("Time (s)")
#simulate acoustic feekback, point-by-point
# _______________ _______________
# clean speech: x --> mic: x1 --> | Internal Gain | --> x2 -- > speaker : y--> | Room Impulse |
# ^ |______G________| |____Response___|
# | |
# ----------------------<-----y1--------------------------------V
#
N = min(2000, len(rir)) #limit room impulse response length
x2 = np.zeros(N) #buffer N samples of speaker output to generate acoustic feedback
y = np.zeros(len(x)) #save speaker output to y
y1 = 0.0 #init as 0
for i in range(len(x)):
x1 = x[i] + y1
y[i] = G*x1
y[i] = min(2, y[i]) #amplitude clipping
y[i] = max(-2, y[i])
x2[1:] = x2[:N-1]
x2[0] = y[i]
y1 = np.dot(x2, rir[:N])
sf.write(howling_file, y, Srate)
plt.figure()
plt.subplot(2,1,1)
plt.plot(y)
plt.xlim((0, len(y)))
plt.subplot(2,1,2)
plt.specgram(y, NFFT=nFFT, Fs=Srate, noverlap=len2, cmap='jet')
plt.ylim((0, 5000))
plt.ylabel("Frquency (Hz)")
plt.xlabel("Time (s)")
#notch filter
fs = Srate # Sample frequency (Hz)
f0 = 603 # Frequency to be removed from signal (Hz)
Q = 1 # Quality factor
# Design notch filter
b1, a1 = signal.iirnotch(f0, Q, fs)
sos1 = np.append(b1,a1)
#plot_notch_filter(b1, a1, fs)
f0 = 1745 # Frequency to be removed from signal (Hz)
Q = 5 # Quality factor
# Design notch filter
b2, a2 = signal.iirnotch(f0, Q, fs)
sos2 = np.append(b2,a2)
#plot_notch_filter(b2, a2, fs)
sos = np.vstack((sos1,sos2))
b, a = signal.sos2tf(sos)
plot_notch_filter(b, a, fs)
#=============================Notch Filtering =======================================================
# _______________ ______________
# clean speech: x --> mic: x1 --> | Internal Gain |-x2--> | Notch Filter | --> speaker : y
# ^ |______G________| |_____IIR______| |
# | |
# | _______________ |
# <-----------------y1--| Room Impulse |____________________ v
# |____Response___|
#
N = min(2000, len(rir)) #limit room impulse response length
x2 = np.zeros(len(b)) #
x3 = np.zeros(N) #buffer N samples of speaker output to generate acoustic feedback
y = np.zeros(len(x)) #save speaker output to y
y1 = 0.0 #init as 0
for i in range(len(x)):
x1 = x[i] + y1
x2[1:] = x2[:len(x2)-1]
x2[0] = G*x1
x2[0] = min(1, x2[0]) #amplitude clipping
x2[0] = max(-1, x2[0])
y[i] = np.dot(x2, b) - np.dot(x3[:len(a)-1], a[1:]) #IIR filter
x3[1:] = x3[:N-1]
x3[0] = y[i]
y1 = np.dot(x3, rir[:N])
xfinal = y
sf.write(output_file, xfinal, Srate)
plt.figure()
plt.subplot(2,1,1)
plt.plot(xfinal)
plt.xlim((0, len(xfinal)))
plt.subplot(2,1,2)
plt.specgram(xfinal, NFFT=nFFT, Fs=Srate, noverlap=len2, cmap='jet')
plt.ylim((0, 5000))
plt.ylabel("Frquency (Hz)")
plt.xlabel("Time (s)")
plt.show()
if __name__=="__main__":
main()