Digital Tuner
Post on 02-Jan-2016
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The notes in the music are distinguished by their frequency
The note in 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 2
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 = 2x Hz, where X = 5, 6, 7, 8, 9… for the sake of simplicity.
EE 113D Fall 2008 3
C = 32 Hz, 64 Hz, 128 Hz, 256 Hz, etc.
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.
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Human vocal range: 80-1100 Hz Piano note frequency range: 27.5 – 4186 Hz Human hearing 20 Hz – 20 KHz
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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.
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Output signal magnitude generation is exponential:
|2x -2x+a|, -0.5<a<0.5
Since notes are base 2 logarithmic, not linear
C – 7th octave
C – 8th octave
C – 6th octave
Our output signal varies exponentiallywith the input signal’s relative distancefrom the tuning frequency.
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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 locationB is the total number of indices
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Simulation: generated a sine wave
Testing: generated sine wave from function generator
Real Life: microphone signal input
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Simulation: FFT function in matlab
Testing: RFFT.asm files from experiment 5. Uses a Radix-2, DIT
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Simulation: Loop through array find max frequency
Testing: getfreq.asm file uses finds max frequency index and converts it
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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
Simulation: Scaling max frequency to known scale: ~16khz
Testing: thold.asm file performs a series of bitwise shifts to scale to reference freq.
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Simulation: Scaling max frequency to known scale: ~16khz
Testing: thold.asm file performs a series of bitwise shifts to scale to reference freq.
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Simulation: Compare to tuning key and output ratio
Testing: thold.asm implements lookup table for comparison and lookup table for resultEE 113D Fall 2008 16
Simulation: Compare to tuning key and output ratio
Testing: thold.asm implements lookup table for comparison and lookup table for resultEE 113D Fall 2008 17
Lookup .word 11585, 11994, 12417, 12855 .word 13308, 13777, 14263, 14766 .word 15287, 15826, 16384, 16962 …
Magnup .word 16, 32, 64, 128 .word 256, 512, 1024, 2048 .word 4096, 8192, 16384, 8192
…
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.
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FFT algorithm does not work with a voice input from microphone. ◦ Further testing required.
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Tune to multiple notes Determine what note is being played Convert output to sheet music Play sheet music
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