Other Research

For this project, the most important audio analysis tool that I used was Harmonic Product Spectrum as my pitch detection algorithm. For musical notes and similar input signals, HPS is an extremely robust method of pitch detection. As applied to this research, it was tested against a pure tone "middle C" and it correctly identified its pitch to within 2%.



Another method of pitch detection that was described in Youngmoo Edmund Kim's Thesis entitled, "Singing Voice Analysis/Synthesis" is pitch detection using the Autocorrelation function.

Principally, this algorithm exploits the fact that periodic signals are similar from one period to the next. As a result, the algorithm only requires that you window the signal and take the autocorrelation of the signal. By differentiating this data set and searching for the minima, you can find the fundamental period (and thus the frequency) of the windowed signal.

Kim choose this method because it was computationally inexpensive. In addition, since his actual task involved speech coding, he needed the autocorrelation values for each frame for his LPC calculations.

Two other pitch detection techniques that are common in Speech Analysis applications but were not used in musical applications were Zero Crossing Rate and Cepstral coefficients.

(Image credit - http://cnx.org/content/m11714/latest/)