Band limited vs Non band limited

There are some ugens which have a band-limited and non-band-limited versions in SC. For instance the Pulse ugen generate band-limited signal, whereas LFPulse creats non-band-limited pulses. What is the difference between band-limited and non-limited ugens? At least to my ear both Pulse and LFPulse sound exactly the same.

// warning: high pitch

{ * -20.dbamp ! 2 }.play;

{ ( - 0.5) * -20.dbamp ! 2 }.play;

So, the reason this is hearable in the higher frequencies, is that LFPulse passes through also other frequencies near the center frequency?


What you’re hearing is aliasing, an unavoidable mathematical fact of digital audio. There should be many pages online about it – it’s a fundamental concept.


Thanks, to my understanding aliasing is about relation between sampling frequency and the signal, how could it be related to the filters and filter bands? That confuses me!

Try this: foldover

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There’s a temptation to think that aliasing can be filtered away. That is only possible in very special cases. It’s like a spice in a meal that you cannot simply remove after you have added it – you might like the flavour though :slight_smile:


This might help you understand - it is in Pd but it explains in the most musician-friendly why harmonics will fold at Nyquist.


Interesting to plot them - shows some clear differences:

{[,]}.plot(0.01, bounds: 800 @ 500, minval: -1, maxval: 1);
{[,]}.plot(0.01, bounds: 800 @ 500, minval: -1, maxval: 1);

Make sure you use a frequency that isn’t an integer subharmonic of the sample rate. You won’t get a complete picture if the fundamental is 400 hz and the SR is e.g. 48000

There is a miscalculation in the example here, right? The foldover signal should be 11 Khz?

This is indeed interesting! The band-limited signals seem to have an amplitude in the range -0.5, 0.5

Do you know why is this the case?

for example if one tries to generate a 13,000 Hz. signal when the sampling rate is 11,000 HZ then the signal will be folded over to 9,000 HZ. (11,000 - 2,000)

Indeed @amt, this is incorrect.

Corrected: “for example if one tries to generate a 13,000 Hz. signal when the Nyquist frequency is 11,000 HZ (sampling rate = 22000 Hz) then the signal will be folded over to 9,000 HZ. (11,000 - 2,000 – also, 22000 - 13000 = 9000)”


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Very interesting (and subtle) point.

A pulse waveform that is exactly balanced, e.g. n samples up, n samples down, has no aliasing.


b = { / 16, mul: 2, add: -1) }.getToFloatArray(1024 / s.sampleRate, action: { |data| d = data.postln });

// this pure square wave doesn't need a window
// but the next one will need it
// So let's keep things apples-to-apples

d = d * Signal.hanningWindow(d.size);

// FFT
f =, Signal.fftCosTable(d.size));
f = f.asPolar;

f.rho[0 .. d.size div: 2].plot;

… and you’ll see four harmonics. This is as expected, according to the Nyquist theorem: frequency = sample rate / 16 = Nyquist / 8, so in theory you could see harmonics 1 to 8. But a square wave includes only odd harmonics, so you see 1, 3, 5 and 7.

The plot uses a linear frequency scale, so it isn’t clear that it’s a high frequency – but it is. Find the index of that first peak, and then scale it up to the sample rate:

i = f.rho.detectIndex(_ > 200);  // 63

i * s.sampleRate / 1024  // at 48kHz, 2953.125

… which is very close to the input frequency. (The Hann window must be affecting the pitch slightly.)

If the frequency is not an integer factor of the sample rate, then the up-vs-down cycles can’t be balanced.

b = { / 16.2, mul: 2, add: -1) }.getToFloatArray(1024 / s.sampleRate, action: { |data| d = data.postln });

// 8s with occasional 9s != _).collect(_.size)

Most of the blocks of consecutive equivalent values are 8 samples long, but sometimes one is 9 samples.

In order to stretch/push/squeeze the samples into that irregular shape, the spectrum must contain extra frequencies… and we see them. Repeat the FFT part:

d = d * Signal.hanningWindow(d.size);

f =, Signal.fftCosTable(d.size));

f = f.asPolar;

f.rho[0 .. d.size div: 2].plot

… and you see a LOT of junk.

So aliasing is related to the asymmetry in the unbalanced waveform.

When the frequency is very high, the push/pull is a relatively large percentage of the wavelength – so the alias frequencies must be strong enough to hear. At lower frequencies, it’s a much smaller percentage, and accordingly, the amount of energy in aliased frequencies is significantly less (maybe inaudible).



It’s easier to think of this in the frequency domain. If the fundamental is 100, and Nyquist is 10 kHz, then the frequencies above Nyquist that get folded over will coincide exactly with the harmonics and don’t produce inharmonic artifacts (although in general will not produce identical results to an actual antialiased signal).

I’m not sure why. I tried forcing the range to (-1,1), but it doesn’t seem to make a difference to the range seen on the plot:

{[,, 1)]}.plot(0.01, bounds: 800 @ 500, minval: -1, maxval: 1);
{[,, 1)]}.plot(0.01, bounds: 800 @ 500, minval: -1, maxval: 1);

I have an interesting graphical demonstration of that: A few years ago, I hacked up a GUI that oversamples a 32-point signal. If you put a geometrically perfect 32-sample square wave into it:

// run the big block, then:

// square = 16x +0.7, 16x -0.7 = Array.fill(z div: 2, [0.7, -0.7]).lace(z);

… it looks like a true bandlimited square, complete with Gibbs effect, in between the samples.

But… pull the FFT out of that, and normalize it against its maximum magnitude, and:

e.fft.rho / e.fft.rho.maxItem

-> [ 0.0, 1.0, 0.0, 0.33765868917828, 0.0, 0.20792919291452, 0.0, 0.15450533035176, 0.0, 0.12679923770497, 0.0, 0.11114046832904, 0.0, 0.10242762415687, 0.0, 0.09849141074536, 0.0, 0.09849141074536, 0.0, 0.10242765224407, 0.0, 0.11114046686992, 0.0, 0.12679924945131, 0.0, 0.15450531860542, 0.0, 0.2079291879641, 0.0, 0.33765864269487, 0.0, 0.99999992864426 ]

… in fact, there is some excess energy. The third partial should be 1/3, but is 0.33766 (about 0.004 too high); the fifth partial should be 1/5, but is 0.20793 (0.008 too high).

I hadn’t noticed this before.

In the GUI, you can also “correct” the FFT and display it:

p = e.fft.deepCopy;
m = p.rho.maxItem; { |mag, i| p.rho[i] = if(mag < 0.001) { 0.0 } { m / max(i, 1) } };

e.fft = p;

… and then the samples go just a little bit wiggly.

A common trick for synthesizing a pulse wave is to subtract a delayed copy of a sawtooth. When the pulse width is 0.5, this will leave about half the amplitude.

	var freq = 391, pulseWidth = 0.5;
	var saw =;
	[,, pulseWidth),
		saw -, 1, pulseWidth / freq)
}.plot(0.01, bounds: 800 @ 500, minval: -1, maxval: 1);

Here, the amplitudes are about the same between the Pulse vs saw - DelayC (although Pulse seems to take more time for DC to stabilize).


Let SC do it for you automatically!
(Please correct me if the arithmetic below is wrong!)

	var freq, sr, in, freqFollowed, hasFreq;
	sr =;
	in =;
	# freqFollowed, hasFreq =, minFreq: 16, maxFreq: sr / 2);
	freq =, sr);
	[freq, freqFollowed, sr - freq].poll; * 0.01;

I am not sure about the information in the video: he says if playing a 23050 Hz tone on a machine with 44100 Hz sr, what we hear are two tones: one at 23050 Hz and the other at 21050 Hz?

This one doesn’t exist at all. It’s already folded over. It’s gone.

A sampled function cannot represent any frequency higher than half the sample rate, period, no exception. This is a 100% inescapable mathematical reality. At 44100 Hz, there is no 23000-anything Hz.