Adding White Gaussian noise to a voice signal

1. Why do we add white Gaussian noise?
2. How can I make a white noise signal?
3. How do you add noise to a signal?
4. How do I add Gaussian noise to signal?

Why do we add white Gaussian noise?

Additive white Gaussian noise (AWGN) is a basic noise model used in information theory to mimic the effect of many random processes that occur in nature. The modifiers denote specific characteristics: Additive because it is added to any noise that might be intrinsic to the information system.

How can I make a white noise signal?

White noise may be generated digitally with a digital signal processor, microprocessor, or microcontroller. Generating white noise typically entails feeding an appropriate stream of random numbers to a digital-to-analog converter. The quality of the white noise will depend on the quality of the algorithm used.

How do you add noise to a signal?

You can use randn() to generate a noise vector 'awgnNoise' of the length you want. Then, given a specified SNR value, calculate the power of the orignal signal and the power of the noise vector 'awgnNoise'. Get the right amplitude scaling factor for the noise vector and just scale it.

How do I add Gaussian noise to signal?

y = awgn( x , snr ) adds white Gaussian noise to the vector signal x . This syntax assumes that the power of x is 0 dBW. For more information about additive white Gaussian noise, see What is AWGN? y = awgn( x , snr , signalpower ) accepts an input signal power value in dBW.