5.4. Linear Filtering with FFT#

  • Consider passing a signal \(x[n]\) of length \(N\) to an FIR Filter with impulse response \(h[n]\) of length \(L\). In practice, we often have \(N \gg L \gg 1\). We will adopt this assumption in the discussion below.

  • Direct implementation of the convolution \(y[n] = h[n]*x[n]\) requires \(\mathcal{O}(NL)\) complex multiplications (and additions). While performing the filtering operation in the frequency domain using FFT algorithms to calculate DFT and IDFT as suggested in Section 5.2.3 requires \(\mathcal{O}(M\log_2 M)\) complex multiplications, where \(M \geq N+L-1\). Since \(N \gg L\), the computational complexity of this frequency-domain filtering approach is thus \(\mathcal{O}(N\log_2 N)\). Hence, if \(L \gg \log_2 N\), then we may obtain a significant reduction in computational complexity by performing frequency-domain filtering using FFT.

  • Nevertheless, the FFT implementation requires \(\mathcal{O}(N\log_2 N)\) complex storage units, as compared to the \(\mathcal{O}(N)\) required for direct convolution. The additional amount of storage requirement may be excessive if \(N\) is very large for say some embedded systems which may have limited amount of memory.

  • More importantly, frequency-domain filtering using FFT is a block processing approach. That is, one can not produce any output until the whole block of \(N\) input samples becomes available. This introduces a large latency in the filtering processing if \(N\) is large; thus preventing the applicability of the frequency-domain filtering approach in scenarios where real-time, low-latency processing is required.

5.4.1. Overlap-Add Algorithm#

  • The standard solution to the storage and latency problems is to:

    1. break the long \(x[n]\) into consecutive smaller blocks of length \(\tilde{N}\),

    2. perform frequency-domain filtering on each smaller block of length \(\tilde{N}\) with an FFT of a smaller (\(< N\)) size \(\tilde{M}\), and

    3. combine the filter output blocks to obtain the output to the whole \(x[n]\).

  • To filter each smaller input block of length \(\tilde{N}\), one may choose the FFT size \(\tilde{M}\) satisfying \(\tilde{M} \geq \tilde{N} + L -1\) so that the output block contains the valid convolution result of length \(\tilde{N} + L -1\). The filtered output to the whole \(x[n]\) can then be obtained by superimposing overlapped blocks of output corresponding to consecutive blocks of input as shown in the diagram below:

    Overlap-add algorithm

    This approach to perform frequency-domain filtering is often referred to as the overlap-add algorithm.

  • Since we need to perform filtering on \(\frac{N}{\tilde{N}}\) input blocks and each block requires \(\mathcal{O}(\tilde{M} \log_2 \tilde{M})\) calculations, the computational complexity of filtering the whole input signal is this \(\mathcal{O}\left( \frac{N\tilde{M}}{\tilde{N}} \log_2 \tilde{M} \right)\) and the storage requirement is \(\tilde{M} \log_2 \tilde{M}\). For instance, if we choose \(\tilde{N} \sim L\) and \(\tilde{M} = \tilde{N}+L-1\), then the computational complexity becomes \(\mathcal{O}(N\log_2 L)\) and the storage requirement becomes \(\mathcal{O}(N+ L\log_2 L)\). Comparing with direct convolution, we can still get a significant reduction in computational complexity as long as \(L \gg 1\). The storage requirement is significantly lowered, compared with applying frequency-domain filtering with a single large-size FFT on the whole \(x[n]\). The filter latency reduces to \(\mathcal{O}(L)\).

5.4.2. Overlap-Save Algorithm#

  • Another approach is to:

    1. break the long \(x[n]\) into consecutive overlapping blocks of length \(\tilde{M} \geq L\),

    2. perform frequency-domain filtering on each input block of length \(\tilde{M}\) with an FFT of size \(\tilde{M}\), and

    3. combine the filter output blocks to obtain the output to the whole \(x[n]\).

  • Since the length of the input block is the same as the FFT size, the frequency-domain filtering output gives us the circular convolution \((h \circledast_{\tilde{M}} x)[n]\) rather than the convolution \((h*x)[n]\) in this case. Referring back to (5.4), \((h\circledast_{\tilde{M}} x)[n]\) is the simply the periodic extension of \((h*x)[n]\), whose length is \(\tilde{M}+L-1\). Thus, it is easy to check that the last \(\tilde{M}-L+1\) samples in \((h\circledast_{\tilde{M}} x)[n]\) are not corrupted by time aliasing in the periodic extension process. That is, we have \((h\circledast_{\tilde{M}} x)[n] = (h*x)[n]\) for \(n=L-1, L, \ldots, \tilde{M}-1\). As a result, we may still be able to obtain the convolution output \(y[n]\) to the whole \(x[n]\) by concatenating the uncorrupted portions of the circular convoluted output blocks as long as the input blocks overlap by \(L-1\) samples. This leads to the overlap-save algorithm as depicted below:

    Overlap-save algorithm

  • It is also easy to check that the same orders of computational complexity, storage requirement, and latency of the overlap-add algorithm can be achieved by the overlap-save algorithm as well.