 Overlap–save method

Overlap–save is the traditional name for an efficient way to evaluate the discrete convolution between a very long signal x[n] and a finite impulse response (FIR) filter h[n]:

(
where h[m]=0 for m outside the region [1, M].
The concept is to compute short segments of y[n] of an arbitrary length L, and concatenate the segments together. Consider a segment that begins at n = kL + M, for any integer k, and define:
Then, for kL + M ≤ n ≤ kL + L + M − 1, and equivalently M ≤ n − kL ≤ L + M − 1, we can write:
The task is thereby reduced to computing y_{k}[n], for M ≤ n ≤ L + M − 1.
Now note that if we periodically extend x_{k}[n] with period N ≥ L + M − 1, according to:
the convolutions and are equivalent in the region M ≤ n ≤ L + M − 1. So it is sufficient to compute the point circular (or cyclic) convolution of with in the region [1, N]. The subregion [M, L + M − 1] is appended to the output stream, and the other values are discarded.
The advantage is that the circular convolution can be computed very efficiently as follows, according to the circular convolution theorem:
where FFT and IFFT refer to the fast Fourier transform and inverse fast Fourier transform, respectively, evaluated over N discrete points.
Contents
Pseudocode
(Overlap–save algorithm for linear convolution) H = FFT(h,N) i = 1 while i <= Nx il = min(i+N1,Nx) yt = IFFT( FFT(x(i:il),N) * H, N) y(i : i+NM) = yt(M : N) i = i+NM+1 end
Efficiency
The pseudocode above requires about 2N log_{2}(N) + N complex multiplications for the FFT, product of arrays, and IFFT. Each iteration produces NM+1 output samples, so the number of complex multiplications per output sample is about:

(
For example, when M=201 and N=1024, Eq.2 equals 26, whereas direct evaluation of Eq.1 would require up to 201 complex multiplications per output sample, the worst case being when both x and h are complexvalued. Also note that for any given M, Eq.2 has a minimum with respect to N. It diverges for both small and large block sizes.
Overlap–discard
Overlap–discard and Overlap–scrap are less commonly used labels for the same method described here. However, these labels are actually better (than overlap–save) to distinguish from overlap–add, because both methods "save", but only one discards. "Save" merely refers to the fact that M − 1 input (or output) samples from segment k are needed to process segment k + 1.
Extending overlap–save
The overlapsave algorithm may be extended to include other common operations of a system ^{[1]}^{[2]}:
 additional channels can be processed more cheaply than the first by reusing the forward FFT
 sampling rates can be changed by using different sized forward and inverse FFTs
 frequency translation (mixing) can be accomplished by rearranging frequency bins
Notes
References
 Rabiner, Lawrence R.; Gold, Bernard (1975), Theory and application of digital signal processing, Englewood Cliffs, N.J.: PrenticeHall, pp. 65–67, ISBN 0139141014
 Borgerding, Mark (2006), "Turning OverlapSave into a Multiband Mixing, Downsampling Filter Bank", IEEE Signal Processing Magazine (March 2006): 158–161, http://www.3dblabs.com/01598092_MultibandFilterbank.pdf
 US patent 6898235, Carlin, Joe; Terry Collins & Peter Hays et al., "Wideband communication intercept and direction finding device using hyperchannelization", published 1999, issued 2005
Categories: 
Wikimedia Foundation. 2010.
Look at other dictionaries:
Overlapsave method — Overlap save is the traditional name for an efficient way to evaluate the discrete convolution between a very long signal x [n] with a finite impulse response (FIR) filter h [n] ::egin{align}y [n] = x [n] * h [n] stackrel{mathrm{def{=} sum {m=… … Wikipedia
Overlap–add method — The overlap–add method (OA, OLA) is an efficient way to evaluate the discrete convolution of a very long signal x[n] with a finite impulse response (FIR) filter h[n]: where h[m]=0 for m outside the region [1, M]. The concept is to divide the… … Wikipedia
Overlapadd method — The overlap add method (OA, OLA) is an efficient way to evaluate the discrete convolution between a very long signal x [n] with a finite impulse response (FIR) filter h [n] ::egin{align}y [n] = x [n] * h [n] stackrel{mathrm{def{=} sum {m=… … Wikipedia
List of numerical analysis topics — This is a list of numerical analysis topics, by Wikipedia page. Contents 1 General 2 Error 3 Elementary and special functions 4 Numerical linear algebra … Wikipedia
Window function — For the term used in SQL statements, see Window function (SQL) In signal processing, a window function (also known as an apodization function or tapering function[1]) is a mathematical function that is zero valued outside of some chosen interval … Wikipedia
List of mathematics articles (O) — NOTOC O O minimal theory O Nan group O(n) Obelus Oberwolfach Prize Object of the mind Object theory Oblate spheroid Oblate spheroidal coordinates Oblique projection Oblique reflection Observability Observability Gramian Observable subgroup… … Wikipedia
Circular convolution — The circular convolution, also known as cyclic convolution, of two aperiodic functions occurs when one of them is convolved in the normal way with a periodic summation of the other function. That situation arises in the context of the… … Wikipedia
Discrete Fourier transform — Fourier transforms Continuous Fourier transform Fourier series Discrete Fourier transform Discrete time Fourier transform Related transforms In mathematics, the discrete Fourier transform (DFT) is a specific kind of discrete transform, used in… … Wikipedia
Dingo — For other uses, see Dingo (disambiguation). Dingo Australian dingo Conservation status … Wikipedia
BIBLE — THE CANON, TEXT, AND EDITIONS canon general titles the canon the significance of the canon the process of canonization contents and titles of the books the tripartite canon … Encyclopedia of Judaism