On Sun, Dec 10, 2006 at 12:35:13PM +0000, James Courtier-Dutton wrote: This is rather ambiguous. What do you mean by 'at 20%' ? Most noise signals do not have a clearly defined peak amplitude. Gaussian noise, even with finite power, has an infinite peak amplitude ! So you should always use the RMS value to indicate a noise level. Not exactly. The error will not be symmetric around the sine amplitude as the average power of the noise is not zero. When you convert the complex FFT output to a real value by x*x+y*y, you effectively measure power. Even if you take the square root of this afterwards, it's still power, expressed in amplitude units. The value in the signal bin is the signal power plus the noise power in that bin, which will fluctuate around its mean value, and that mean value is not zero. Yes. Assuming the noise is white, its power will on average be divided evenly over all the FFT bins. So the more bins, the less noise there will be in each of them. But you never reach zero, as that would require an infinite length FFT. No. It's average power is not zero. It decreases because there are more bins, and the sum of the powers in all bins must be the power of the signal. If you average the powers measured by a number of shorter FFTs (each one using 'new' noise of course), then the noise power in each bin will converge to P / (N / 2), where P is the total noise power and N the FFT length. Subtract this from the power in the bin where your signal is, and you get the exact signal power. -- FA Lascia la spina, cogli la rosa.