add_noise.py

import math
import librosa
import matplotlib.pyplot as plt
import numpy as np
from scipy.io.wavfile import write

def get_white_noise(signal,SNR) :
    #RMS value of signal
    RMS_s=math.sqrt(np.mean(signal**2))
    #RMS values of noise
    RMS_n=math.sqrt(RMS_s**2/(pow(10,SNR/20)))
    #Additive white gausian noise. Thereore mean=0
    #Because sample length is large (typically > 40000)
    #we can use the population formula for standard daviation.
    #because mean=0 STD=RMS
    STD_n=RMS_n
    noise=np.random.normal(0, STD_n, signal.shape[0])
    return noise

#***convert complex np array to polar arrays (2 apprays; abs and angle)
def to_polar(complex_ar):
    return np.abs(complex_ar),np.angle(complex_ar)

signal_file = 'sound/input.wav'
signal, sr = librosa.load(signal_file)
signal=np.interp(signal, (signal.min(), signal.max()), (-1, 1))
noise=get_white_noise(signal,SNR=10)
plt.plot(signal)
plt.show()

#analyze the frequency components in the signal
X=np.fft.rfft(noise)
radius,angle=to_polar(X)
plt.plot(radius)
plt.xlabel("FFT coefficient")
plt.ylabel("Magnitude")
plt.show()
signal_noise=signal+noise
plt.plot(signal_noise)
plt.xlabel("Sample number")
plt.ylabel("Amplitude")
plt.show()

write("sound/input_with_noise.wav",sr,signal_noise)