domain characteristics to pre-defined frequency maps (useful for detecting pitch of fixed tuning instruments); and the detection of peaks due to harmonic series. To improve on the pitch estimate derived from the discrete Fourier spectrum, techniques such as spectral reassignment (phase based) or Grandke interpolation (magnitude based) can be used to go beyond the precision provided by the FFT bins. Another phase-based approach is offered by Brown and Puckette Spectral/temporal approaches Spectral/temporal pitch detection algorithms, e.g. the YAAPT pitch tracking algorithm, are based upon a combination of time domain processing using an autocorrelation function such as normalized cross correlation, and frequency domain processing utilizing spectral information to identify the pitch. Then, among the candidates estimated from the two domains, a final pitch track can be computed using dynamic programming. The advantage of these approaches is that the tracking error in one domain can be reduced by the process in the other domain. Speech pitch detection The fundamental frequency of speech can vary from 40 Hz for low-pitched voices to 600 Hz for high-pitched voices. Autocorrelation methods need at least two pitch periods to detect pitch. This means that in order to detect a fundamental frequency of 40 Hz, at least 50 milliseconds (ms) of the speech signal must be analyzed. However, during 50 ms, speech with higher fundamental frequencies may not necessarily have the same fundamental frequency throughout the window. See also Auto-Tune Beat detection Frequency estimation Linear predictive coding MUSIC (algorithm) Sinusoidal model References External links Alain de Cheveigne and Hideki Kawahara: YIN, a fundamental frequency estimator for speech and music AudioContentAnalysis.org: Matlab code for various pitch detection algorithms Audio engineering Digital signal processing