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Pre and Post-Processing for Pitch Tracking

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

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Pre and Post-Processing for Pitch Tracking

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  1. 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

  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

  12. Unreliable Pitch Removal (2/2) • Pitch removal via volume/clarity thresholding

  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 autocorrelationfor茉莉花 (jasmine)

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

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