If you’ve ever looked at a hi-fi component stereo system you’ve probably seen a graphic equalizer. Traditional analog ones look like this one sold by Crutchfield:

Each slider lets you raise or lower the volume of certain sounds in your music. If you know that a flute is in a frequency range of 250 Hz - 2500 Hz, you could make the flute part louder by raising the sliders that corresponded to that range. (As you see from this acoustics chart, frequency ranges for instruments do overlap—so raising a flute can possibly also affect other things like Oboes or vocals.)

Cheaper stereo systems usually only offer “bass” and “treble” knobs. Yet nearly every MP3 player has a digital EQ. Even iTunes has one, if you click Show Equalizer on the View menu:

But here’s a problem: because graphic equalizers break your signals into pieces and then put them together again, they introduce some distortion. There’s a simple way to get a sense that this distortion exists: you can set all the sliders to “flat”… run the equalizer algorithm… and see that you don’t get the same data out that you put in.

Back in 1996, I came up with a way you can design a digital graphic equalizer so the algorithm will exactly output the original signal when sliders are flat, even after running your audio through a barrage of filtering mathematics. Practically speaking, you would want to bypass the equalizer in that case for performance reasons. Yet there is an alluring elegance to an approach which would converge to the original signal if you ran it… and it strongly suggests you’d have overall less distortion when the sliders weren’t at zero. (This turns out to be true!)

I employ only two digital filters: a specially matched highpass and lowpass. Recursively applying these filters on downsampled versions of the data creates the exact logarithmic bands that are found in graphic equalizers. The process additionally leads to a phenomenon known as “perfect reconstruction”. I explain the details below.

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