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 output

  • In 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 filter

  • IIR 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 good

  • Where 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.

Last modified: Tuesday, 14 April 2020, 6:48 PM