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resample | Examples See Also |

Change sampling rate by any factor.

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)

```
y = resample(x,p,q)
```

resamples the sequence in vector `x`

at `p/q`

times the original sampling rate, using a polyphase filter implementation. The length of `y`

is equal to `ceil(length(x)*p/q)`

. `p`

and `q`

must be positive integers. 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 `fir1`

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 be`ta`

is 5.
```
y = resample(x,p,q,b)
```

filters `x`

with `b`

, a vector of filter coefficients.
```
[y,b] = resample(x,p,q)
```

returns the vector `b`

, which contains the coefficients of the filter applied to `x`

during the resampling process.
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))Notice that the last few points of the output`*`

2/(3`*`

Fs1); % new time vector plot(t`1,x,'*',t2`

,y,'o',-0.5:0.01:1.5,-0.5:0.01:1.5,':') legend('original','resampled')

`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); plot(1:19,x,'If`*`

',(0:28)`*`

2/3 + 1,y,'o') x = [10:-1:1 2:10]; y = resample(x,3,2); plot(1:19,x,'`*`

',(0:28)`*`

2/3 + 1,y,'o')

`p`

or `q`

are not positive integers, `resample`

gives the appropriate error message:
P must be a positive integer. Q must be a positive integer.If

`x`

is not a vector, `resample`

gives the following error message:
Input X must be a vector.

`decimate` |
Decrease the sampling rate for a sequence (decimation). |

`fir1` |
Window-based finite impulse response filter design--standard response. |

`interp` |
Increase sampling rate by an integer factor (interpolation). |

`interp1` |
One-dimensional data interpolation (table lookup) (see the online MATLAB Function Reference). |

`intfilt` |
Interpolation FIR filter design. |

`kaiser` |
Kaiser window. |

`spline` |
Cubic spline interpolation (see the online MATLAB Function Reference). |

`upfirdn` |
Upsample, apply an FIR filter, and downsample. |