Difference between revisions of "Hilbert-Huang Transform"

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(Created page with '=== How to do it (in plain english) === # Start by finding all local peaks and valleys of the audio data. The trick is: Everytime you see a sample that is taller than both the n…')
 
(How to do it (in plain english))
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# Do a "nice" interpolation between all the valleys. Let's call this the "min curve".
 
# Do a "nice" interpolation between all the valleys. Let's call this the "min curve".
 
# Create a resulting "mean curve" by averaging min and max: mean = (min+max)/2
 
# Create a resulting "mean curve" by averaging min and max: mean = (min+max)/2
# Save the resulting "mean curve" as the first iteration.
+
# Save the resulting "mean curve" as the first (or second or third) iteration.
 
# Subtract the mean from the original audio data, and jump back to 1.
 
# Subtract the mean from the original audio data, and jump back to 1.
  
 
Repeat this 4 to 8 times (According to Professor Huang.)
 
Repeat this 4 to 8 times (According to Professor Huang.)

Revision as of 23:19, 22 November 2010

How to do it (in plain english)

  1. Start by finding all local peaks and valleys of the audio data. The trick is: Everytime you see a sample that is taller than both the next and the previous sample, it's a peak. Everytime you see a sample that is lower than both the next and the previous sample, it's a valley.
  2. Do a "nice" interpolation between all the peaks. Let's call this the "max curve".
  3. Do a "nice" interpolation between all the valleys. Let's call this the "min curve".
  4. Create a resulting "mean curve" by averaging min and max: mean = (min+max)/2
  5. Save the resulting "mean curve" as the first (or second or third) iteration.
  6. Subtract the mean from the original audio data, and jump back to 1.

Repeat this 4 to 8 times (According to Professor Huang.)