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Pitch Tracking

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  1. MUMT 611 Philippe Zaborowski February 2005 Pitch Tracking

  2. Pitch Tracking • Goal is to track the fundamental • Vast area of research mostly focused on voice coding • Dozens of different algorithms • All algorithms have limitations • None are ideal

  3. Technical Difficulties: Piano

  4. Technical Difficulties: E. Bass

  5. Algorithm Classification • Time Domain • Spectral Domain • Combined Time/Spectral Domain • Neural Networks

  6. Time Domain • Common Features: • Analysis performed on sample basis instead of buffered intervals • No transformation needed • Cheap on computation • Common Drawbacks: • Not suited for signals where the fundamental is weak and the harmonics are strong • DC offset can be a problem

  7. Time Domain • Threshold Crossing (zero crossing)

  8. Time Domain • Dolansky (1954)

  9. Time Domain • Rabiner and Gold (1969)

  10. Time Domain • Autocorrelation (Rabiner 1977)

  11. Time Domain • Average Magnitude Difference Function (Ross 1974)

  12. Time Domain • Cooper and Ng (1994)

  13. Time/Spectral Domain • Least-Square (Choi 1995) • Combines the reliability of frequency-domain with high resolution of time-domain • Able to analyze shorter signal segments • Suitable for real-time • Uses constant Q tranform

  14. Spectral Domain • Common Features: • Transformation from time to spectral domain is computationally intensive • Superior control and analysis of formants • Common Drawbacks: • Simple study of spectrum not enough • DFT based algorithms use equally spaced bins

  15. Spectral Domain • FFT with different harmonic analysis: • Maximum of FFT (Division Method) • Piszczalski and Galler (1979) • Harmonic Product (Schroeder 1968)

  16. Spectral Domain • Constant Q transform (Brown and Puckette 1992)

  17. Spectral Domain • Cepstrum (Andrews 1990)

  18. Conclusion • Spectral Domain: • Give good results • Require a demanding analysis of spectrum • Time Domain: • Generally inferior to spectral domain • Some have comparable results with less computation

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