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Pitch Tracking

MUMT 611 Philippe Zaborowski February 2005. Pitch Tracking. 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. Technical Difficulties: Piano.

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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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