## Simple FIR Lowpass Filter

• Let's design an FIR lowpass filter

• First, some notation: x(n) is the nth sample of input, y(n) is the nth sample of output. Amplitude of sample is assumed -1..1

• Filter equation:

  y(n) = (x(n) + x(n - 1)) / 2

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

• Common in analog, but almost never in digital

## 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?

## Inversion, Reversal, Superposition

• Why the obsession with lowpass? 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.

## Convolution

• A filter can be thought of as a convolution of the input signal: sum of possibly delayed weighted inputs

• Convolution is probably out of scope for this course, but pretty cool

• 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 "Windowing" Filters

• In general, simplest low-pass filters: take a "window" of past samples, then "round off the corners" by multiplying by some symmetric transfer function

• There are many window functions, each with their own slightly different properties as filters: simple things like triangular, plausible things like cosine, and weird things like Blackman, Hamming, Hanning

• Note that windowing is also how we deal with edge effects of DFT: we make the signal have period equal to the DFT size by applying a window, but this also low-passes and changes the signal

## FIR Chebyshev "Remez Exchange" Filters

• There's a fancy mathematical trick for approximating a given desired filter shape with high accuracy for a given filter size

• Involves treating filter coefficients as coefficients of a Chebyshev Polynomial, then adjusting the coefficients until maximum error is minimized

• Probably not something you want to do yourself, but there are programs out there that will do it for you

## IIR Filters

• Can get much better response per unit computation by feeding the filter output back into the filter (?!)

• In some applications, a 12th-order IIR filter can replace a 1024th-order FIR filter

• Design of these filters really wants a full understanding of complex analysis, outside the scope of this course

• Fortunately, many standard filter designs exist: Chebyschev, Bessel, Butterworth, Biquad, etc

• Basic operation is the same as FIR, except that you have to remember some output:

  y(n) = (1/(k+m)) (x(n-k … n) ∙ a(k … 0) + y(n-m-1 … n-1) ∙ a(0 … m))

• Always use floating point, as intermediate terms can get large / small

• Really, just look up a filter design and implement it: probably too hard to "roll your own"