Decorrelating Stationary Colored Gaussian Noise -- Effect On The Desired Signal So given stationary colored gaussian noise $\mathbf{n}$, I know that I can decorrelate it by first finding it's autocorrelation $R_{nn}$ and performing $R^{-\frac{1}{2}}_{nn} \mathbf{n}$. 

In practice of course, I need to estimate $R_{nn}$, which I can do via averaging $\mathbf{r}_{nn}$ or by fitting it to an AR model. 

So, suppose I have a good estimate $\hat{R}_{nn}$, for a finite data record of length $N$. Such that the resulting spectrum of the noise frame is white enough, according to some metric. 

Now, suppose I have a signal $\mathbf{x} = \mathbf{s} + \mathbf{n}$. I want to decorrelate the noise, such that after the decorrelation, I get a signal in white noise. From what I have read, the way to do this is to also apply $R^{-\frac{1}{2}}_{nn} \mathbf{x}$. 

However, won't this distort the desired signal $\mathbf{s}$? Can anyone point me to some references regarding this? 

Also: How else can I decorrelate colored noise in order to get my signal embedded in white noise?
Yes, the "Whitening" is basically filtering the signal using an LTI System.

The Result would be the signal $ \mathbf{s} $ filtered by the system which whitens the noise.

In the framework of Matched Filter this is OK since you will be able to filter the Reference Signal as well and hence have the same performance.

Yet for other usages you must take under consideration the effect of whitening system.