# lowpass

Lowpass-filter signals

## Syntax

## Description

filters the input signal `y`

= lowpass(`x`

,`wpass`

)`x`

using a lowpass filter with
normalized passband frequency `wpass`

in units of
*π* rad/sample. `lowpass`

uses a
minimum-order filter with a stopband attenuation of 60 dB and compensates for
the delay introduced by the filter. If `x`

is a matrix, the
function filters each column independently.

specifies additional options for any of the previous syntaxes using name-value
pair arguments. You can change the stopband attenuation, the Lowpass Filter Steepness, and the
type of impulse response of the filter.`y`

= lowpass(___,`Name,Value`

)

`[`

also returns the `y`

,`d`

] = lowpass(___)`digitalFilter`

object
`d`

used to filter the input.

`lowpass(___)`

with no output arguments plots
the input signal and overlays the filtered signal.

## Examples

### Lowpass Filtering of Tones

Create a signal sampled at 1 kHz for 1 second. The signal contains two tones, one at 50 Hz and the other at 250 Hz, embedded in Gaussian white noise of variance 1/100. The high-frequency tone has twice the amplitude of the low-frequency tone.

fs = 1e3; t = 0:1/fs:1; x = [1 2]*sin(2*pi*[50 250]'.*t) + randn(size(t))/10;

Lowpass-filter the signal to remove the high-frequency tone. Specify a passband frequency of 150 Hz. Display the original and filtered signals, and also their spectra.

lowpass(x,150,fs)

### Lowpass Filtering of Musical Signal

Implement a basic digital music synthesizer and use it to play a traditional song. Specify a sample rate of 2 kHz. Plot the spectrogram of the song.

fs = 2e3; t = 0:1/fs:0.3-1/fs; l = [0 130.81 146.83 164.81 174.61 196.00 220 246.94]; m = [0 261.63 293.66 329.63 349.23 392.00 440 493.88]; h = [0 523.25 587.33 659.25 698.46 783.99 880 987.77]; note = @(f,g) [1 1 1]*sin(2*pi*[l(g) m(g) h(f)]'.*t); mel = [3 2 1 2 3 3 3 0 2 2 2 0 3 5 5 0 3 2 1 2 3 3 3 3 2 2 3 2 1]+1; acc = [3 0 5 0 3 0 3 3 2 0 2 2 3 0 5 5 3 0 5 0 3 3 3 0 2 2 3 0 1]+1; song = []; for kj = 1:length(mel) song = [song note(mel(kj),acc(kj)) zeros(1,0.01*fs)]; end song = song/(max(abs(song))+0.1); % To hear, type sound(song,fs) pspectrum(song,fs,'spectrogram','TimeResolution',0.31, ... 'OverlapPercent',0,'MinThreshold',-60)

Lowpass-filter the signal to separate the melody from the accompaniment. Specify a passband frequency of 450 Hz. Plot the original and filtered signals in the time and frequency domains.

```
long = lowpass(song,450,fs);
% To hear, type sound(long,fs)
lowpass(song,450,fs)
```

Plot the spectrogram of the accompaniment.

figure pspectrum(long,fs,'spectrogram','TimeResolution',0.31, ... 'OverlapPercent',0,'MinThreshold',-60)

### Lowpass Filter Steepness

Filter white noise sampled at 1 kHz using an infinite impulse response lowpass filter with a passband frequency of 200 Hz. Use different steepness values. Plot the spectra of the filtered signals.

fs = 1000; x = randn(20000,1); [y1,d1] = lowpass(x,200,fs,'ImpulseResponse','iir','Steepness',0.5); [y2,d2] = lowpass(x,200,fs,'ImpulseResponse','iir','Steepness',0.8); [y3,d3] = lowpass(x,200,fs,'ImpulseResponse','iir','Steepness',0.95); pspectrum([y1 y2 y3],fs) legend('Steepness = 0.5','Steepness = 0.8','Steepness = 0.95')

Compute and plot the frequency responses of the filters.

[h1,f] = freqz(d1,1024,fs); [h2,~] = freqz(d2,1024,fs); [h3,~] = freqz(d3,1024,fs); plot(f,mag2db(abs([h1 h2 h3]))) legend('Steepness = 0.5','Steepness = 0.8','Steepness = 0.95')

## Input Arguments

`x`

— Input signal

vector | matrix

Input signal, specified as a vector or matrix.

**Example: **`sin(2*pi*(0:127)/16)+randn(1,128)/100`

specifies a noisy
sinusoid

**Example: **`[2 1].*sin(2*pi*(0:127)'./[16 64])`

specifies a two-channel
sinusoid.

**Data Types: **`single`

| `double`

**Complex Number Support: **Yes

`wpass`

— Normalized passband frequency

scalar in (0, 1)

Normalized passband frequency, specified as a scalar in the interval (0, 1).

`fpass`

— Passband frequency

scalar in (0, `fs`

/2)

Passband frequency, specified as a scalar in the interval (0, `fs`

/2).

`fs`

— Sample rate

positive real scalar

Sample rate, specified as a positive real scalar.

`xt`

— Input timetable

timetable

Input timetable. `xt`

must contain increasing, finite, and equally spaced
row times of type `duration`

in seconds.

If a timetable has missing or duplicate time points, you can fix it using the tips in Clean Timetable with Missing, Duplicate, or Nonuniform Times.

**Example: **`timetable(seconds(0:4)',randn(5,1),randn(5,2))`

contains a
single-channel random signal and a two-channel random signal, sampled at 1 Hz for 4
seconds.

### Name-Value Arguments

Specify optional
comma-separated pairs of `Name,Value`

arguments. `Name`

is
the argument name and `Value`

is the corresponding value.
`Name`

must appear inside quotes. You can specify several name and value
pair arguments in any order as
`Name1,Value1,...,NameN,ValueN`

.

**Example:**

`'ImpulseResponse','iir','StopbandAttenuation',30`

filters the input using a minimum-order IIR filter that attenuates frequencies
higher than `fpass`

by 30 dB.`ImpulseResponse`

— Type of impulse response

`'auto'`

(default) | `'fir'`

| `'iir'`

Type of impulse response of the filter, specified as the comma-separated pair consisting of `'ImpulseResponse'`

and `'fir'`

, `'iir'`

, or `'auto'`

.

`'fir'`

— The function designs a minimum-order, linear-phase, finite impulse response (FIR) filter. To compensate for the delay, the function appends to the input signal*N*/2 zeros, where*N*is the filter order. The function then filters the signal and removes the first*N*/2 samples of the output.In this case, the input signal must be at least twice as long as the filter that meets the specifications.

`'iir'`

— The function designs a minimum-order infinite impulse response (IIR) filter and uses the`filtfilt`

function to perform zero-phase filtering and compensate for the filter delay.If the signal is not at least three times as long as the filter that meets the specifications, the function designs a filter with smaller order and thus smaller steepness.

`'auto'`

— The function designs a minimum-order FIR filter if the input signal is long enough, and a minimum-order IIR filter otherwise. Specifically, the function follows these steps:Compute the minimum order that an FIR filter must have to meet the specifications. If the signal is at least twice as long as the required filter order, design and use that filter.

If the signal is not long enough, compute the minimum order that an IIR filter must have to meet the specifications. If the signal is at least three times as long as the required filter order, design and use that filter.

If the signal is not long enough, truncate the order to one-third the signal length and design an IIR filter of that order. The reduction in order comes at the expense of transition band steepness.

Filter the signal and compensate for the delay.

`Steepness`

— Transition band steepness

`0.85`

(default) | scalar in the interval [0.5, 1)

Transition band steepness, specified as the comma-separated pair
consisting of `'Steepness'`

and a scalar in the
interval [0.5, 1). As the steepness increases, the filter response
approaches the ideal lowpass response, but the resulting filter length
and the computational cost of the filtering operation also increase. See
Lowpass Filter Steepness
for more information.

`StopbandAttenuation`

— Filter stopband attenuation

`60`

(default) | positive scalar in dB

Filter stopband attenuation, specified as the comma-separated pair consisting of `'StopbandAttenuation'`

and a positive scalar in dB.

## Output Arguments

`y`

— Filtered signal

vector | matrix | timetable

Filtered signal, returned as a vector, a matrix, or a timetable with the same dimensions as the input.

`d`

— Lowpass filter

`digitalFilter`

object

Lowpass filter used in the filtering operation, returned as a `digitalFilter`

object.

Use

**FVTool**to visualize the filter response.Use

`designfilt`

to edit or generate a digital filter based on frequency-response specifications.

## More About

### Lowpass Filter Steepness

The `'Steepness'`

argument controls the width
of a filter's transition region. The lower the steepness, the wider the transition
region. The higher the steepness, the narrower the transition region.

To interpret the filter steepness, consider the following definitions:

The

*Nyquist frequency*,*f*_{Nyquist}, is the highest frequency component of a signal that can be sampled at a given rate without aliasing.*f*_{Nyquist}is 1 (×*π*rad/sample) when the input signal has no time information, and`fs`

/2 hertz when the input signal is a timetable or when you specify a sample rate.The

*stopband frequency*of the filter,*f*_{stop}, is the frequency beyond which the attenuation is equal to or greater than the value specified using`'StopbandAttenuation'`

.The

*transition width*of the filter,*W*, is*f*_{stop}–`fpass`

, where`fpass`

is the specified*passband frequency*.Most nonideal filters also attenuate the input signal across the passband. The maximum value of this frequency-dependent attenuation is called the

*passband ripple*. Every filter used by`lowpass`

has a passband ripple of 0.1 dB.

When you specify a value, *s*, for
`'Steepness'`

, the function computes the transition width as

*W* = (1 – *s*) ×
(*f*_{Nyquist} –
`fpass`

).

When

`'Steepness'`

is equal to 0.5, the transition width is 50% of (*f*_{Nyquist}–`fpass`

).As

`'Steepness'`

approaches 1, the transition width becomes progressively narrower until it reaches a minimum value of 1% of (*f*_{Nyquist}–`fpass`

).The default value of

`'Steepness'`

is 0.85, which corresponds to a transition width that is 15% of (*f*_{Nyquist}–`fpass`

).

## See Also

### Apps

### Functions

**Introduced in R2018a**

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