Acyclic FFT Convolution
If we add enough trailing zeros to the signals being convolved, we can obtain acyclic convolution embedded within a cyclic convolution. How many zeros do we need to add? Suppose the signal consists of contiguous nonzero samples at times 0 to , preceded and followed by zeros, and suppose is nonzero only over a block of samples starting at time 0. Then the acyclic convolution of with reduces to
which is zero for and . Thus,
The number is easily checked for signals of length 1 since , where is 1 at time zero and 0 at all other times. Similarly,
and so on.
When or is infinity, the convolution result can be as small as 1. For example, consider , with , and . Then . This is an example of what is called deconvolution. In the frequency domain, deconvolution always involves a pole-zero cancellation. Therefore, it is only possible when or is infinite. In practice, deconvolution can sometimes be accomplished approximately, particularly within narrow frequency bands .
We thus conclude that, to embed acyclic convolution within a cyclic convolution (as provided by an FFT), we need to zero-pad both operands out to length , where is at least the sum of the operand lengths (minus one).
In Matlab or Octave, the conv function implements acyclic convolution:
octave:1> conv([1 2],[3 4]) ans = 3 10 8Note that it returns an output vector which is long enough to accommodate the entire result of the convolution, unlike the filter primitive, which always returns an output signal equal in length to the input signal:
octave:2> filter([1 2],1,[3 4]) ans = 3 10 octave:3> filter([1 2],1,[3 4 0]) ans = 3 10 8
Figure 8.2 shows schematically the result of convolving two zero-padded signals and . In this case, the signal starts some time after , say at . Since begins at time 0 , the output starts promptly at time , but it takes some time to ``ramp up'' to full amplitude. (This is the transient response of the FIR filter .) If the length of is , then the transient response is finished at time . Next, when the input signal goes to zero at time , the output reaches zero samples later (after the filter ``decay time''), or time . Thus, the total number of nonzero output samples is .
If we don't add enough zeros, some of our convolution terms ``wrap around'' and add back upon others (due to modulo indexing). This can be called time-domain aliasing. Zero-padding in the time domain results in more samples (closer spacing) in the frequency domain, i.e., a higher `sampling rate' in the frequency domain. If we have a high enough spectral sampling rate, we can avoid time aliasing.
The motivation for implementing acyclic convolution using a zero-padded cyclic convolution is that we can use a Cooley-Tukey Fast Fourier Transform (FFT) to implement cyclic convolution when its length is a power of 2.
Acyclic FFT Convolution in Matlab
Cyclic FFT Convolution