## Digital Audio Filters

## Digital Filters

Idea: get signal into system as close to Nyquist as possible

Do filtering mostly in software (or digital hardware)

Can build

*much*better filters

## Aside: Number Representation

How shall we represent samples for this kind of processing?

Obvious choice: integers at sampling resolution

Can get weird for 24-bit, so promote to 32?

Math is tricky: overflow etc. Promote to next higher size?

What resolution to output? May have more or less precision than started with

Fast

Obvious choice: floating-point

Scale input to -1..1 or 0..1

32 or 64 bit? (32-bit conveniently has 24 bits of precision)

Issues of precision and resolution

*mostly*go away (Inf and NaN).Fast with HW support, slow otherwise especially on 8-bit hardware

Less obvious choice: "fixed-point"

Treat integer as having implicit fixed "binary point"

`.1001011000000001 1.001011000000001 -.001011000000001 10010110.00000001`

Fiddly, especially for languages that don't allow implementing a fixed-point type with normal arithmetic

Slightly slower than integer: must keep the decimal in the right place

Typical used on integer-only embedded systems, "DSP chips"

Strongly suggest 64-bit floating point for this course: just say no to a bunch of annoying bugs

## DFT Filters

Obvious approach: Convert to frequency domain, scale the frequencies you don't want, convert back

For real-time filter output, this in principle means doing a DFT and inverse DFT at every sample position, which seemsâ€¦expensive to get one sample out

Can cheat by sliding the window more than one, but you will lose time information from your signal

Also, DFT has

*ripple*: frequencies between bin centers will be slightly lower than they should be, since they are split between two bins and the sum of gaussians there isn't quite 1Frequency resolution can be an issue: a 128-point FFT on a 24KHz sample will produce roughly 200Hz bins, so the passband is going to be something like 400Hz, which is significant

## FIR and IIR Filters

We characterize filters in terms of

*impulse response*: what if you have an input sample consisting of a single pulse of amplitude 1 and then zeros forever?Taking a look at the DFT sum, our DFT filter will treat an impulse anywhere in its window identically (linear time-invariant). When the pulse leaves the window, the FFT will then say 0 forever

We call this

*Finite Impulse Response*: an impulse presented to the filter will eventually go awayA trick that we will explore is to actually use past filter

*outputs*as well as inputs to decide the next filter outputIn this case, an impulse will make it into the outputs, which means that it will be looped back into the inputs:

*Infinite Impulse Response*Of course, the IIR filter should reduce the amplitude of the impulse over time, else badness. Such a filter is a

*stable*filterIIR filters have cheap implementation (analog or digital) per unit quality, but:

Are less flexible

Are harder to design

Have lots of issues with stability, noise, numerics