Music Technology 101: Dithering Explained (1/2) – Quantization Noise e technology 101

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In this two-part video tutorial I will explain dithering from the ground up. For your convenience, here are the links to the two parts:
Part 1:
Part 2:

You do not need any special background in signal processing, audio or dithering to follow the current videos. However, you should know what bit depth means. If you don’t, fear not! Just watch my short video tutorial about bit-depth and sampling rates right here:

What’s in Part 1: Dithering is all about getting rid of quantization noise. What is quantization noise? Glad you’ve asked, because that’s exactly what we’re going to cover in the first part! Shortly put, quantization noise is the noise introduced whenever we reduce the bit depth of our signal. For example, most audio is recorded using 24 bits of resolution, but modern audio CDs only have 16 bits of resolution, implying that a reduction in bit depth must be applied. This reduction will introduce some artifacts known as quantization error, or quantization noise. This “noise” will have some jarring, unpleasant frequency components which we’d like to get rid of.

What’s in Part 2: In the second part we will cover dithering. To “dither” a signal means to add some form of random noise to it because lowering its bit depth. This dither noise has a beneficial effect: while it doesn’t eliminate quantization noise, it gives it a more random, “white” nature which is less disturbing to the listener. When the amount of white noise equals approximately 1 bit in magnitude, the quantization error becomes a lot like white noise. This is because quantization involves rounding the input signal either up or down. When the noise becomes on the order of 1 bit, the rounding becomes random, and therefore the quantization error becomes random as well.

DITHERING TYPES

Dithering requires that we add random noise to our signal before downsampling. This noise should have a flat spectrum – in other words, be white. However, there is more than one way to generate white random noise. Probably the easiest and most efficient way is to use what’s known as a triangular probability distribution function, or TPDF. You might have seen these initials in your dithering plugin. This is an excellent way to efficiently dither. Although we won’t discuss the heavy mathematical theory of dithering in this video, I’ll just mention that TPDF white noise decouples the first and second moments of the quantization noise.

Another option consists of using noise with a non-flat spectrum. For example, you’d might add noise that has more high-frequency components, such as Blue Noise. This is referred to as shaped noise, shaped noise dithering, or colored dithering. What this tries to do is force the dithered quantization noise to occupy higher frequencies that are outside the human audio range. Once again, personal experimentation is key to deciding whether you want to use colored dithering or not, but this is truly a very fine point. You will be fine if you just stick to TPDF. However, a word of caution: only apply colored dithering at the FINAL stage of your processing. If you need to dither audio at some point DURING mixing, use TPDF. This is because subsequent processing of the audio can cause the colored noise to creep into the audible listening range and create nasty artifacts. So: Use TPDF at all stages before mixing, and use TPDF or colored dithering during the final mixdown.

LINKS OF INTEREST

Here is a wonderful guide to dithering written in 2002 by Nika Aldrich, targeted at the audio engineer:

This is truly geared towards the audio enthusiast and does not go into any math. It is heavily illustrated and references industry standard plugins such as Apogee’s UV22.

Wikipedia’s entry on dithering:

My Other Videos

My Youtube channel has many other video tutorials covering various topics in both audio and music, mostly geared towards piano playing. Here are a few examples:
Bit depth and sample rate explained:
Song writing Tips and Tricks – Rhythmic Doubling:
Reading Sheet Music for Beginners:
The 2-5-1 Harmonic Progression Tutorial:
Playing Left hand Piano Arpeggios:
An Exercise for Developing Piano Right-Hand Technique: .

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19 comments

Pedro Vega 28/09/2021 - 9:21 Chiều

OMG so well explained!!!! thank you very much!!!

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Paradeisa 28/09/2021 - 9:21 Chiều

thanks a lot!

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Peka Pekanov 28/09/2021 - 9:21 Chiều

Thank you!

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575garden 28/09/2021 - 9:21 Chiều

fantastic video

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William Russotto 28/09/2021 - 9:21 Chiều

Brilliant explanation

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HellaHipHop 28/09/2021 - 9:21 Chiều

the dark arts. ahhhh my love

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Mike Preset 28/09/2021 - 9:21 Chiều

that was so clearly explained ..Thank you

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EL Switch 28/09/2021 - 9:21 Chiều

Love it!!

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GingerBagels 28/09/2021 - 9:21 Chiều

I just don't get it…

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Lukas Mitterecker 28/09/2021 - 9:21 Chiều

great video! absolutly helpful!

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Joseph Woods 28/09/2021 - 9:21 Chiều

Great video, concise with excellent examples on to part 2

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Sam Worskett 28/09/2021 - 9:21 Chiều

Excellent video – perfectly explained. Thanks!

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Andrew Turner 28/09/2021 - 9:21 Chiều

Thanks Buddy. Excellent!

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Abraham Munguia 28/09/2021 - 9:21 Chiều

This is real altruism.

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Alis A 28/09/2021 - 9:21 Chiều

Thank you. Once again, very concise and understandable.

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sticksquash 28/09/2021 - 9:21 Chiều

Don't most players have some sort of interpolation on bit depth and sample rate to make it sound more smoother, or curve it as opposed to say connecting the dots (linear) or using the nearest neighbor? Probably through a cubic interpolation? It would at least make it resemble a sinusoidal but might make more complex waveforms more muddy.

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iBrade 28/09/2021 - 9:21 Chiều

Only 1 minute in and i find it more interesting than what my lecture teaches me.

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redirishmanxlt 28/09/2021 - 9:21 Chiều

If I render a track to WAV using 48khz and 24bit, would there be any need for dithering?

What I'm trying do is render an entire track to audio, and then master it with dithering.  

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Alex Kozar 28/09/2021 - 9:21 Chiều

Great explanations here. It's very interesting to see the details behind quantisation noise

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