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"

    0.100101100000001   =~  0.585968017578125
    1.001011000000001   =~ -0.171905517578125 (sign-magnitude)
                        =~ -0.828094482421875 (twos-complement)
    
    10010110.00000001   =~ -105.99609375 (twos-complement)
    

  • 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 seems 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 1

• Frequency 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