resample (Signal Processing Toolbox)

Change sampling rate by any rational factor

Syntax

y = resample(x,p,q)
y = resample(x,p,q,n)
y = resample(x,p,q,n,beta)
y = resample(x,p,q,b)
[y,b] = resample(x,p,q)

Description

y = resample(x,p,q) resamples the sequence in vector x at p/q times the original sampling rate, using a polyphase filter implementation. p and q must be positive integers. The length of y is equal to ceil(length(x)*p/q). If x is a matrix, resample works down the columns of x.

resample applies an anti-aliasing (lowpass) FIR filter to x during the resampling process. It designs the filter using firls with a Kaiser window.

y = resample(x,p,q,n) uses n terms on either side of the current sample, x(k), to perform the resampling. The length of the FIR filter resample uses is proportional to n; larger values of n provide better accuracy at the expense of more computation time. The default for n is 10. If you let n = 0, resample performs a nearest-neighbor interpolation

```
y(k) = x(round((k-1)*q/p)+1)
```

where y(k) = 0 if the index to x is greater than length(x).

y = resample(x,p,q,n,beta) uses beta as the design parameter for the Kaiser window that resample employs in designing the lowpass filter. The default for beta is 5.

y = resample(x,p,q,b) filters x using the vector of filter coefficients b.

[y,b] = resample(x,p,q) returns the vector b, which contains the coefficients of the filter applied to x during the resampling process.

Examples

Resample a simple linear sequence at 3/2 the original rate:

fs1 = 10;            % Original sampling frequency in Hz
t1 = 0:1/fs1:1;      % Time vector
x = t1;              % Define a linear sequence
y = resample(x,3,2); % Now resample it
t2 = (0:(length(y)-1))*2/(3*fs1); % New time vector
plot(t1,x,'*',t2,y,'o',-0.5:0.01:1.5,-0.5:0.01:1.5,':')
legend('original','resampled'); xlabel('Time')

Notice that the last few points of the output y are inaccurate. In its filtering process, resample assumes the samples at times before and after the given samples in x are equal to zero. Thus large deviations from zero at the end points of the sequence x can cause inaccuracies in y at its end points. The following two plots illustrate this side effect of resample:

x = [1:10 9:-1:1]; y = resample(x,3,2);
subplot(2,1,1);
plot(1:19,x,'*',(0:28)*2/3 + 1,y,'o');
title('Edge Effects Not Noticeable');
legend('original','resampled');
x = [10:-1:1 2:10]; y = resample(x,3,2);
subplot(2,1,2);
plot(1:19,x,'*',(0:28)*2/3 + 1,y,'o')
title('Edge Effects Very Noticeable');
legend('original','resampled');

Algorithm

resample performs an FIR design using firls, followed by rate changing implemented with upfirdn.

See Also

decimate, downsample, firls, interp, interp1, intfilt, kaiser, spline, upfirdn, upsample

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