Understanding Pitch Detection with Autocorrelation Methods I've been reading through A Smarter Way to Find Pitch, describing its pitch detection algorithm using autocorrelation.

I've been having trouble understanding the accuracy claims. It says:


  MPM runs in real time with a standard 44.1 kHz sampling rate. It operates without using low-pass filtering so
  it can work on sound with high harmonic frequencies such
  as a violin and it can display pitch changes of one cent reliably.


I cannot fathom how detecting changes of one cent is possible. Considering a note played at 441Hz, sampled at 44.1kHz, one would measure a first peak at tao=100 samples (delay space).

The next closest note which could be measured, by my understanding, would have a peak at the 101st sample. This would be a frequency of 437Hz, or about 16 cents lower than the first note. What am I missing here?
For higher resolution periodicity estimation, given properly band-limited data, the data can be upsampled (interpolated) before autocorrelation, and/or, the autocorrelation results can be interpolated (Sinc kernel or some order of polynomial).  

This, of course, assumes the signal has a high enough S/N, and that the periodicity itself is unmodulated in timbre(harmonic content), phase, frequency, or amplitude, which isn’t always true of real-world pitched sounds.  Thus a larger number of periods may be needed for a some desired statistical error bound on the pitch estimation.