Pitch Tracking MUMT 611 Philippe Zaborowski February 2005

Pitch Tracking

MUMT 611Philippe 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