Fundamental Frequency Estimation from Sinusoidal Peaks

Sinusoidal peak measurement was discussed in Chapter 4. Given a set of sinusoidal peak frequencies , , it is usually straightforward to form a fundamental frequency estimate ``''. This task is also called pitch detection, where the perceived ``pitch'' of the audio signal is assumed to coincide well enough with its fundamental frequency. We assume here that the signal is periodic, so that all of its sinusoidal components are harmonics of the fundamental frequency . (For inharmonic sounds, the perceived pitch, if any, can be complex to predict [54].)

An approximate maximum-likelihood -detection algorithm^10.1 consists of the following steps:

1. Find the peak of the histogram of the peak-frequency-differences in order to find the most common harmonic spacing. This is the nominal pitch estimate.
2. Refine the nominal pitch estimate using linear regression. Linear regression simply fits a straight line through the data to give a least-squares fit.
3. The slope of the fitted line gives the pitch estimate.

A matlab listing for F0 estimation along these lines appears in §G.6.

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Subsections

• Useful Preprocessing
• Getting Closer to Maximum Likelihood
• References on Estimation

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About the Author: Julius Orion Smith III
Julius Smith's background is in electrical engineering (BS Rice 1975, PhD Stanford 1983). He is presently Professor of Music and Associate Professor (by courtesy) of Electrical Engineering at Stanford's Center for Computer Research in Music and Acoustics (CCRMA), teaching courses and pursuing research related to signal processing applied to music and audio systems. See http://ccrma.stanford.edu/~jos/ for details.