1. Pre and Post-Processing for Pitch Tracking
Jyh-Shing Roger Jang (張智星)
MIR Lab, Dept of CSIE
National Taiwan University

2. Preprocessing for Pitch Tracking
Some commonly used preprocessing for the audio signals before pitch tracking
Pre-filtering the signals
Clipping the signals
SIFT method for the signals

3. Preprocessing: Pre-filtering
Observation
Range of humans’ pitch: [40, 1000]
Idea
Low-pass the signals with a cutoff frequency between 800 and 1000
Characteristics
The effect is yet to be verified

4. Preprocessing: Clipping
Observation
Small signals near zero is likely to cause pitch tracking error
Idea
Clip the signals
Characteristics
Save computation for embedded system
Overall effect is yet to be verified

5. Preprocessing: SIFT Observation
Channel effect is likely to cause pitch tracking error
Idea of SIFT (simple inverse filter tracking)
Identify the excitation via LPC
Use the excitation for PDF
Characteristics
Overall effect is yet to be verified

6. Example of SIFT
siftAcf01.m

7. Example of PT based on SIFT & ACF
ptBySiftAcf01.m

8. Postprocessing for Pitch Tracking
Some commonly used postprocessing for pitch tracking
Smoothing to remove abrupt-changing pitch
Interpolation to increase pitch precision

9. Postprocessing: Smoothing
Smoothing by a median filter
ptWithMedianFilter01.m

10. Postprocessing: Interpolation
Idea
Using the pitch point and its neighbors to identify the max position
ptWithParabolicFit01.m

11. Unreliable Pitch Removal (1/2)
Pitch removal via volume thresholding
Plot by self demo of ptByPf.m

12. Unreliable Pitch Removal (2/2)
Pitch removal via volume/clarity thresholding
Plot by self demo of ptByPf.m

13. Rest Handling Original pitch vectors with rests.
Rests are replaced by previous nonzero pitch. Good for LS.
Rests are removed. Good for DTW.

14. Typical Result of Pitch Tracking
Pitch tracking via autocorrelation for茉莉花 (jasmine)

15. Comparison of Pitch Vectors
Yellow line : Target pitch vector