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Digital Tuner


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  • 1. EE 113D Fall 2008 Patrick Lundquist Ryan Wong
  • 2.
    • The notes in the music are distinguished by their frequency
    • The note of each octave is twice the frequency of the same note in the previous octave.
    • Ex: C = 32.7 Hz, 65.4 Hz, 130.8 Hz, 261.6 Hz, 523.2 Hz … etc.
    EE 113D Fall 2008
  • 3.
    • The frequencies of the C notes are actually 32.7 Hz, 65.4 Hz, 130.8 Hz, 261.6 Hz… etc.
    • But we use C = 2 x Hz, where X = 5, 6, 7, 8, 9… for the sake of simplicity.
    EE 113D Fall 2008
  • 4. Output signal magnitude generation is exponential: |2 x -2 x+a |, -0.5<a<0.5 Since notes are base 2 logarithmic, not linear C – 7 th octave C – 8 th octave C – 6 th octave Our output signal varies exponentially with the input signal’s relative distance from the tuning frequency. EE 113D Fall 2008
  • 5.
    • A tuner can be aplied to anything that can be measured on a specturm analyzer
    • Ex: instruments, function generator, human voice.
    • We can start testing our finished product with a function generator and then move onto the more complicated human voice.
    EE 113D Fall 2008
  • 6.
    • Human vocal range: 80-1100 Hz
    • Piano note frequency range: 27.5 – 4186 Hz
    • Human hearing 20 Hz – 20 KHz
    EE 113D Fall 2008
  • 7.
    • We are going to start with the simplest case
      • Tuning to C (32 Hz, 64 Hz… etc.)
    • We wish to output high if the input is very close to a C in frequency
    • Output will be low if input is anything else.
    • The sampling frequency of the tuner will be 8000 Hz.
      • We chose this frequency because it is twice the maximum frequency of most instruments.
    EE 113D Fall 2008
  • 8.
    • Since we are dealing with frequencies, we know a Fourier Transform will be involved.
      • The rest is just manipulation to get the correct output from various inputs
    • The result of the Fourier Transform is a delta function at a memory index.
      • We calculate frequency based on this index: A/B x F = frequency of signal where F is the sampling frequency, A is the index location B is the total number of indices
    EE 113D Fall 2008
  • 9. EE 113D Fall 2008
  • 10. Simulation: generated a sine wave Testing: generated sine wave from function generator Real Life: microphone signal input EE 113D Fall 2008
  • 11. Simulation: generated a sine wave Testing: sample.asm from lab EE 113D Fall 2008
  • 12. Simulation: FFT function in matlab Testing: RFFT.asm files from experiment 5. Uses a Radix-2, DIT EE 113D Fall 2008
  • 13. Simulation: Loop through array find max frequency Testing: getfreq.asm file uses finds max frequency index and converts it EE 113D Fall 2008
  • 14. Simulation: Scaling max frequency to known scale: ~16khz Testing: thold.asm file performs a series of bitwise shifts to scale to reference freq. EE 113D Fall 2008
  • 15. Simulation: Compare to tuning key and output ratio Testing: thold.asm implements lookup table for comparison and lookup table for result EE 113D Fall 2008
  • 16. Simulation: Scaling max frequency to known scale: ~16khz Testing: thold.asm file EE 113D Fall 2008
  • 17. Sampled signal EE 113D Fall 2008
  • 18. RFFT graphed output EE 113D Fall 2008
  • 19.
    • Integrate all modules into one continuous program.
      • Need to add calling and linking of each module.
      • Timing issues and assembly syntax problems
      • Also, nops and @ operator provided initial trouble.
    • Optimizing program to run in real time.
      • FFT is a time expensive process that reduces the potential for real time tuning.
      • Difficult to determine when FFT is finished running.
    EE 113D Fall 2008
  • 20.
    • Tune to multiple notes
    • Tune to a wider input frequency
    • Record matches to memory or output file
    • Convert output to sheet music
    • Play sheet music
    EE 113D Fall 2008
  • 21.
    • Questions?
    EE 113D Fall 2008