A Quadrature Signals Tutorial: Complex, But Not Complicated

Understanding the 'Phasing Method' of Single Sideband Demodulation

Complex Digital Signal Processing in Telecommunications

Introduction to Sound Processing

Introduction of C Programming for DSP Applications

Overlap-Add (OLA) STFT Processing

Convolving with Long Signals

Overlap-Add Decomposition

**Search Spectral Audio Signal Processing**

**Would you like to be notified by email when Julius Orion Smith III publishes a new entry into his blog?**

Consider breaking an input signal into frames using a finite, zero-phase, length window . Then we may express the th windowed data frame as

The *hop size* is the number of samples between the begin-times
of adjacent frames. Specifically, it is the number of samples by
which we advance each succesive window.

Figure 7.9 shows an input signal (top) and three successive windowed data frames using a length causal Hamming window and 50% overlap ().

For frame-by-frame spectral processing to work, we must be able to reconstruct from the individual overlapping frames, ideally by simply summing them in their original time positions. This can be written as

Hence, if and only if

Figure 7.10 illustrates the appearance of 50% overlap-add for the Bartlett (triangular) window. The Bartlett window is clearly COLA for a wide variety of hop sizes, such as , , and so on, provided is an integer (otherwise the underlying continuous triangular window must be resampled). However, when using windows defined in a library, the COLA condition should be carefully checked. For example, the following Matlab/Octave script shows that there is a problem with the standard Hamming window:

M = 33; % window length R = (M-1)/2; % hop size N = 3*M; % overlap-add span w = hamming(M); % window z = zeros(N,1); plot(z,'-k'); hold on; s = z; for so=0:R:N-M ndx = so+1:so+M; % current window location s(ndx) = s(ndx) + w; % window overlap-add wzp = z; wzp(ndx) = w; % for plot only plot(wzp,'--ok'); % plot just this window end plot(s,'ok'); hold off; % plot window overlap-addThe figure produced by this matlab code is shown in Fig.7.11. As can be seen, the equal end-points sum to form an impulse in each frame of the overlap-add.

The Matlab window functions (such as `hamming`) have an
optional second argument which can be either
`'symmetric'` (the default), or `'periodic'`.
The periodic case is equivalent to

w = hamming(M+1); % symmetric case w = w(1:M); % delete last sample for periodic caseThe periodic variant solves the non-constant overlap-add problem for even and , but not for odd . The problem can be solved for odd and

w = hamming(M); % symmetric case w(1) = w(1)/2; % repair constant-overlap-add for R=(M-1)/2 w(M) = w(M)/2;Since different window types may add or subtract 1 to/from internally, it is best to check the result using test code as above to make sure the window is COLA at the desired hop size.

`hamming(M)`

`.54 - .46*cos(2*pi*(0:M-1)'/(M-1));`

gives constant overlap-add for , , etc., when endpoints are divided by 2 or one endpoint is zeroed`hanning(M)``.5*(1 - cos(2*pi*(1:M)'/(M+1)));`

does*not*give constant overlap-add for , but does for`blackman(M)`

`.42 - .5*cos(2*pi*m)' + .08*cos(4*pi*m)';`

where`m = (0:M-1)/(M-1)`, gives constant overlap-add for when is odd and is an integer, and when is even and is integer.

In summary, all windows obeying the constant-overlap-add constraint will yield perfect reconstruction of the original signal from the data frames by overlap-add (OLA). There is no constraint on window type, only that the window overlap-adds to a constant for the hop size used. In particular, always yields a constant overlap-add for any window function. We will learn later (§7.3.1) that there is also a simple frequency-domain test on the window transform for the constant overlap-add property.

To emphasize an earlier point, if simple time-invariant FIR filtering
is being implemented, and we don't need to work with the intermediate
STFT, it is most efficient to use the *rectangular window* with
hop size , and to set , where is the length of the
filter and is a convenient FFT size. The optimum for a
given is an interesting exercise to work out.

Julius Smith's background is in electrical engineering (BS Rice 1975, PhD Stanford 1983). He is presently Professor of Music and (by courtesy) of Electrical Engineering at Stanford's Center for Computer Research in Music and Acoustics (CCRMA), teaching courses and pursuing research related to signal processing applied to music and audio systems. See http://ccrma.stanford.edu/~jos/ for details.

Comments

No comments yet for this page