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

Last modified: Saturday, 11 April 2020, 2:44 AM