I'm trying to add a white noise to my signal and simulate it for different SNR values. But I'm not sure if i should use randn() or awgn(). For instance I don't understand why these two methods deliver different signals in my code. t=linspace(0,120,8000);
Gaussian white noise (GWN) is a stationary and ergodic random process with zero mean that is defined by the following fundamental property: any two values of GWN are statis- tically independent now matter how close they are in time. The direct implication of this property is that the autocorrelation function of a GWN.
2. I would answer the question bluntly I would say it is because Perlin noise is super simple to get your head around. Simplex noise on the other hand is very much a more complex and hairer beast. Getting a Perlin implementation up and running is much easier than simplex and thus gets more usage.
7. If you filter a Gaussian random process with an LTI system, the output will also be Gaussian. You can make intuitive sense of this by considering that a linear combination (which is what filtering does) of jointly Gaussian random variables is a Gaussian random variable. You can find an in-depth treatment of filtering random processes in this
SNRdb = 10 ∗log10 Psignal Pnoise S N R d b = 10 ∗ log 10 P s i g n a l P n o i s e. Your gaussian noise function generates the noise based on a scaling factor k of the signal max amplitude. Since you want to scale the amplitude of the noise based on your signal, i believe you want a relationship of: k = Anoise Asignal k = A n o i s e A s i
Markov processes + Gaussian processes I Markov (memoryless) and Gaussian properties are di↵erent) Will study cases when both hold I Brownian motion, also known as Wiener process I Brownian motion with drift I White noise ) linear evolution models I Geometric brownian motion ) pricing of stocks, arbitrages, risk neutral measures, pricing of stock options (Black-Scholes)
If you multiply the white noise frequencies by the Gaussian kernel frequencies, you end up with something in the shape of the Gaussian kernel frequencies (low frequencies), but they are randomized. These are the blobs left over after blurring white noise. Figure 5 shows the frequency magnitudes of blue noise, white noise, and a Gaussian blur
Thermal noise in an ideal resistor is approximately white, meaning that the power spectral density is nearly constant throughout the frequency spectrum, but does decay to zero at extremely high frequencies (terahertz for room temperature). When limited to a finite bandwidth, thermal noise has a nearly Gaussian amplitude distribution.
The term "red noise" comes from the "white noise"/"white light" analogy; red noise is strong in longer wavelengths, similar to the red end of the visible spectrum. Strictly, Brownian motion has a Gaussian probability distribution, but "red noise" could apply to any signal with the 1/f 2 frequency spectrum. Power spectrum 4GlnH.
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    • white noise vs gaussian noise