# Normalisation Matters

This page is not (at all) on 'peak to peak normalisation' like it is done in wave editors. Instead, I want to discuss normalisation types for sinusoid vectors in transform matrices. The vector norms have decisive impact on the interpretation of the correlation output. Is the output coefficient an amplitude? Or is it a (co)sine magnitude? It can be one of both, or even something else, depending on how you normalise. Normalisation is not a very popular topic in dsp textbooks. Admittedly, it is rather tedious matter. But time and again confusion about normalisation has returned on me, like a boomerang. I decided to sort it out in detail, with help of plots and spreadsheets.

A square transform matrix contains N vectors, each with N components. The page Fourier Matrix has pictures on that. Below I have sketched a sine and cosine vector, both doing one cycle in 16 samplepoints x[n]. These vectors are the examples that we are going to normalise. Notice that, while N=16, indexing starts at zero, so the highest index number is 15. First I will check the vector norms as they are, without any normalisation. Recall that a vector norm is computed with 'Pythagoras extended'. First compute the sum of the values squared, which is an inner product. Then take the square root of that, which is the norm. I computed the (co)sine values at all angles (2*pi*n/N). I squared these values, and summed them to find the inner product. These vectors have inner product 8. The square root of 8 is around 2.83, and that is the norm. Pairs of sin(x) and cos(x) can be handled as a complex unit vector of the general form cos(x)+isin(x). Pointwise they always have amplitude 1 together. Two-point unit vectors like these can subsequently be appended, using the normalisation factor: On the page Inner Product these things were illustrated with small matrix examples. Now we can use the routine to append 16 complex unit vectors and make a new unit vector of that. All the sine and cosine values are multiplied by N-(1/2). The result gives norm 0.707107 for each phase. That is 2-1/2 or 0.51/2 of course. Together, the phases in the above spreadsheet are a unit vector: If a square matrix is filled with complex unit vectors, the transpose of that matrix is the inverse. No further scaling is needed.

But, maybe you want a phase in itself to be a unit vector for some reason. Imagine the case that you are not using complex vectors at all. A cosine unit vector is found with an extra factor 21/2 to the N-(1/2): See how easy it is to make unit vectors with (co)sines. If you have a thousand-or-more-point vector, no need to compute the inner product and the norm before you know how to normalise. Just use the known factors.

I stated before that unit vectors in a (square) transform matrix will preserve energy. Let us now check what happens if the unit cosine vector from the above spreadsheet is used as a correlation vector in a transform matrix, while the non-normalised cosine from the first spreadsheet is the correlated input vector: The norm of the correlated vector now appears as the correlation coefficient. Below is a plot of this correlation process. For different N, the coefficient reflecting a full cosine would vary. Here is a sheet with N=8 to illustrate that: The preservation of energy may be beneficial in certain situations. But to us, human observators, the coefficients do not make much sense this way. When a transform is used for analysis purposes, other types of normalisation can be used, to yield a more comprehensible output. Let us consider a 1/N normalisation. The spreadsheet below displays that 1/N will not make unit vectors: In fact, normalisation with 1/N is 'norma-normalisation', a double normalisation. Because: Important: when using the factor 1/N in the transform, the non-normalised vectors should be used in the inverse transform. Otherwise you would norma-norma-norma-normalise. Which would result in a very low output.

Let us now do correlation with a 1/N norma-normalised cosine vector, and a full cosine as the input: With 1/N normalisation, half of the cosine input is detected. The reason for sinusoids detecting half the input was discussed in more detail on the Sinusoids Correlation page. If we would do a full complex transform matrix, the other half would be found in the complex conjugate part.

The 1/N normalisation for N=16 is illustrated below with a plot. Compare with the earlier plot, where a unit cosine vector was used. The correlation vector and the product values are much smaller now. For larger N these differences become even more prominent. If we transform real signals only, computing conjugates could be a waste of effort. When normalisation is done with 2/N, a cosine input is fully detected in the correlation, as the next spreadsheet shows. With 2/N normalisation we will find the sine and cosine parts of real signals directly in the correlation coefficients, without further computations.

But now we stumble upon the exceptional cases of DC and half-the-samplerate. In these cases, cosine has double energy and sine has zero energy. On the other hand, they have no conjugates, and should not be computed twice, like the others. If you do not make an exception for them in the normalisation, you will detect twice the cosine that actually went in. Now that I am doing all these spreadsheets, why not illustrate the DC case as well. I am using the same 2/N normalisation here for comparison: And here is cosine for the 'Nyquist case', the half-samplingrate case: Till here we have seen how the coefficients can represent sine and cosine magnitudes. But sometimes, the parameter of interest is the amplitude for any of the frequencies. Here may be a pitfall awaiting the confident engineer. Because, how do we normally compute a radius or amplitude from real and imaginary part? With Pythagoras, nothing can be simpler. But??? That does not represent the amplitude in the signal. Then why is a radius called amplitude? Well it is an amplitude actually, but of a two-point or two-phase vector. While we were examining a one-phase signal, a real signal. To recompute the two-point vector norm to a one point vector norm, we have to normalise one more time with sqrt(1/N), where N=2. So with factor sqrt(1/2).

There is however an option to normalise in such a way that coefficients reflect amplitudes per phase. The normalisation factor is 21/2*(1/N). Here is the sheet of such a case: I want to check if this really works as an amplitude correlator. Here I put a 0.8*cosine vector in: The result shows the amplitude that belongs to 0.8*cosine: 0.8*0.51/2=0.565685 indeed.

Notice that the DC and Nyquist exceptions exist here as well. Because this is a 'reals only' normalisation again, neglecting the presence or existence of conjugates. Look, this will happen to a DC input of amplitude 1: Later, I found that it is pretty hazardous to make statements about amplitudes, based on spectrum correlation coefficients. Because, what the coefficients do not show you, is how components eventually phase-cancel each other in time domain. Therefore, it may not be so useful to know amplitudes at all.

After this spreadsheet overdose, still all is not said about normalisation. I departed from the complex unit vector case, and illustrated how you could do alternative normalisations of sine and cosine phases, to have convenient coefficients for analysis. Employment of sine and cosine in complex vectors is a typical aspect of Fourier Transform. Other transform types, like Discrete Cosine Transform and Wavelet Transform, demand their own normalisation considerations. These matters must remain unclarified here, if only for the reason that I have too little knowledge of it. The boomerang is still in the air.