## FIR Filters

## Convolution

You can think of a particular output of our DFT filter as having been calculated by

*convolution*of a sequence of coefficients with a sequence of input samples$$ X[k] = \sum_{n=0}^{N-1} x[n] e^{-i k n / N} $$

$$ = \sum_{n=0}^{N-1} a[n] x[n] $$

It turns out that this convolution process is standard in filtering: we multiply past input samples by fixed linear coefficients and then add them up to get the current sample value.

Interestingly,

*multiplication in the frequency domain is convolution in the time domain*. This means that we can use a DFT as a convolution operator if we like

## FIR 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 DFT will then say 0 forever

We call this

*Finite Impulse Response*: an impulse presented to the filter will eventually go away

## IIR Filters

A 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

## Simple FIR Lowpass Filter

Let's design an FIR lowpass filter

First, some notation: \(x[i]\) is the ith sample of input, \(y[i]\) is the nth sample of output. Amplitude of sample is assumed -1..1

Filter equation:

$$ y[i] = \frac{x[i] + x[i - 1]}{2} $$

Why is this a low-pass filter? For higher frequencies if sample \(x[i]\) is positive sample \(x[i - 1]\) will tend to be negative, so they will tend to cancel. For lower frequencies the sample \(x[i]\) will be close to \(x[i - 1]\) so they will reinforce

This filter is kind of bad: the frequency response doesn't have much of a "knee" at all

On the other hand, this filter is stupidly cheap to implement, and has very little latency: the output depends only on the current and previous samples

## "Higher Filter Orders"

One way to improve a filter is to cascade copies

Filter functions multiply, but it gets a little weird

## Wider FIR Filters

Normally, you want a much sharper knee

To get that, you typically use more of the history

For standard FIR filters, it is common to use thousands of samples of history

General FIR filter:

$$ y[i] = \frac{1}{k} x[i-k \ldots i] \cdot a[k \ldots 0] $$

So \(k\) multiplications and additions per sample

Now the cost is greater, and the latency is higher, but the quality can be

*very*goodWhere do the coefficients \(a\) come from? Digital filter design, next lecture

## Inversion, Reversal, Superposition

Why the obsession with lowpass? For one, it's the most commonly-used filter in audio

Also because we can get the other kinds "for free" from the lowpass

Inversion: Negate all coefficients and add 1 to the "center" coefficient — this flips the spectrum, so high-pass

Reversal: Reverse the order of coefficients — this reverses the spectrum, so high-pass

Superposition: Average the coefficients of two equal-length filters — this gives a spectrum that is the product of the filters. If one is low-pass and the other high-pass, this is band-notch. We can then invert to get bandpass.