Pitch Tracking MUMT 611 Philippe Zaborowski February 2005

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

Technical Difficulties: E. Bass

Algorithm Classification Time Domain Spectral Domain Combined Time/Spectral Domain Neural Networks

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

Time Domain Threshold Crossing (zero crossing)

Time Domain Dolansky (1954)

Time Domain Rabiner and Gold (1969)

Time Domain Autocorrelation (Rabiner 1977)

Time Domain Average Magnitude Difference Function (Ross 1974)

Time Domain Cooper and Ng (1994)

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

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

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

Spectral Domain Constant Q transform (Brown and Puckette 1992)

Spectral Domain Cepstrum (Andrews 1990)

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