Improving Monophonic Pitch Detection Using the ACF And Simple Heuristics
In this paper a study on the performance of the short time autocorrelation function for the determination of correct pitch candidates for non-stationary sounds is presented. Input segments of a music or speech signal are analyzed by extracting the autocorrelation function and a weighting function is used to weight candidates for assessing their harmonic strength. Furthermore, a decision is devised which alerts if there are possible non-related jumps on the fundamental frequency track. A technique to modify the spectral content of the signal is presented to compensate for these jumps, and a heuristic to return a steady fundamental frequency track for monophonic recordings is presented. The system is evaluated with several databases and with other algorithms. Using the compensation algorithm increases the performance of the ACF and outperforms current detection algorithms.