Pre and Post-Processing for Pitch Tracking Jyh-Shing Roger Jang (張智星) MIR Lab, Dept of CSIE National Taiwan University jang@mirlab.org http://mirlab.org/jang

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

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

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

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

Example of SIFT siftAcf01.m

Example of PT based on SIFT & ACF ptBySiftAcf01.m

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

Postprocessing: Smoothing Smoothing by a median filter ptWithMedianFilter01.m

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

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

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

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

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

Comparison of Pitch Vectors Yellow line : Target pitch vector