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Technical Difficulties: Piano
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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