In signal processing, overlap–save is the traditional name for an efficient way to evaluate the discrete convolution between a very long signal x [ n ] {\displaystyle x[n]} and a finite impulse response (FIR) filter h [ n ] {\displaystyle h[n]} :
where h[m] = 0 for m outside the region [1, M]. This article uses common abstract notations, such as y ( t ) = x ( t ) ∗ h ( t ) , {\textstyle y(t)=x(t)*h(t),} or y ( t ) = H { x ( t ) } , {\textstyle y(t)={\mathcal {H}}\{x(t)\},} in which it is understood that the functions should be thought of in their totality, rather than at specific instants t {\textstyle t} (see Convolution#Notation).
The concept is to compute short segments of y[n] of an arbitrary length L, and concatenate the segments together. That requires longer input segments that overlap the next input segment. The overlapped data gets "saved" and used a second time. First we describe that process with just conventional convolution for each output segment. Then we describe how to replace that convolution with a more efficient method. Consider a segment that begins at n = kL + M, for any integer k, and define:
x k [ n ] ≜ { x [ n + k L ] , 1 ≤ n ≤ L + M − 1 0 , otherwise . {\displaystyle x_{k}[n]\ \triangleq {\begin{cases}x[n+kL],&1\leq n\leq L+M-1\\0,&{\textrm {otherwise}}.\end{cases}}}
y k [ n ] ≜ x k [ n ] ∗ h [ n ] = ∑ m = 1 M h [ m ] ⋅ x k [ n − m ] . {\displaystyle y_{k}[n]\ \triangleq \ x_{k}[n]*h[n]=\sum _{m=1}^{M}h[m]\cdot x_{k}[n-m].}
Then, for k L + M + 1 ≤ n ≤ k L + L + M {\displaystyle kL+M+1\leq n\leq kL+L+M} , and equivalently M + 1 ≤ n − k L ≤ L + M {\displaystyle M+1\leq n-kL\leq L+M} , we can write:
y [ n ] = ∑ m = 1 M h [ m ] ⋅ x k [ n − k L − m ] ≜ y k [ n − k L ] . {\displaystyle y[n]=\sum _{m=1}^{M}h[m]\cdot x_{k}[n-kL-m]\ \ \triangleq \ \ y_{k}[n-kL].}
With the substitution j = n − k L {\displaystyle j=n-kL} , the task is reduced to computing y k [ j ] {\displaystyle y_{k}[j]} for M + 1 ≤ j ≤ L + M {\displaystyle M+1\leq j\leq L+M} . These steps are illustrated in the first 3 traces of Figure 1, except that the desired portion of the output (third trace) corresponds to 1 ≤ j ≤ L. If we periodically extend xk[n] with period N ≥ L + M − 1, according to:
x k , N [ n ] ≜ ∑ ℓ = − ∞ ∞ x k [ n − ℓ N ] , {\displaystyle x_{k,N}[n]\ \triangleq \ \sum _{\ell =-\infty }^{\infty }x_{k}[n-\ell N],}
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![Overlap–save method: Fig 1: A sequence of four plots depicts one cycle of the overlap–save convolution algorithm. The 1st plot is a long sequence of data to be processed with a lowpass FIR filter. The 2nd plot is one segment of the data to be processed in piecewise fashion. The 3rd plot is the filtered segment, with the usable portion colored red. The 4th plot shows the filtered segment appended to the output stream.[A] The FIR filter is a boxcar lowpass with M=16 samples, the length of the segments is L=100 samples and the overlap is 15 samples.](https://upload.wikimedia.org/wikipedia/commons/thumb/e/e4/Overlap-save_algorithm.svg/500px-Overlap-save_algorithm.svg.png?utm_source=en.wikipedia.org&utm_campaign=parser&utm_content=thumbnail)

