# What is the correct length for obtaining a true linear convolution from DFT?

In the linear convolution of two equal length sequences M and N, the length of the output is length(A)+length(B)-1, and if we apply the DFT property of converting convolution into multiplication, the output is equal Max (length (M) or length (N)).

a) If there a simple way to predict how many edge points are contaminated by circular convolution if we were interested in extracting only the linear convolution section from DFT? Suppose, one does not want to do zero padding in DFT to obtain linear convolution.

b) Secondly, if we wish to do true linear convolution via DFT, should we use zero padding up to length(M)+length(N)-1. This is a figure from a PhD thesis obtainable from ResearchGate pg 18, why does it say if the length of DFT >length(M)+length(N)-1, then results are valid. Is the linear convolution not restricted to the length of length(M)+length(N)-1. • crappy use of letters. we almost always use "$N$" for the DFT size. Jan 13, 2021 at 6:33
• and if we apply the DFT property of converting convolution into multiplication, the output is equal Max (length (M) or length (N)). No? That's not true. You need to bring both vectors to the same length before DFT'ing them (before then multiplying them) and if you don't use M+N-1 as common length, you don't get a valid convolution. There's no magic here. Jan 13, 2021 at 6:35

I think what you are asking is: what happens of I implement the convolution of two sequences of length M & N, using spectral multiplication with an FFT of length K? We defined "correct" here as "matching the result of a linear convolution". For simplicity we define the correct length $$L = M + N -1$$