YIN, a fundamental frequency estimator for speech and music

TLDR

The method builds on a periodic signal model that can be extended to handle aperiodicity and has parallels with auditory processing. The study presents an algorithm for estimating the fundamental frequency of speech or musical sounds. The algorithm uses an autocorrelation‑based approach with modifications designed to reduce errors. It achieves about three times lower error rates than leading methods, has no upper frequency limit, and is simple, efficient, low‑latency, and requires few tunable parameters.

Abstract

An algorithm is presented for the estimation of the fundamental frequency (F0) of speech or musical sounds. It is based on the well-known autocorrelation method with a number of modifications that combine to prevent errors. The algorithm has several desirable features. Error rates are about three times lower than the best competing methods, as evaluated over a database of speech recorded together with a laryngograph signal. There is no upper limit on the frequency search range, so the algorithm is suited for high-pitched voices and music. The algorithm is relatively simple and may be implemented efficiently and with low latency, and it involves few parameters that must be tuned. It is based on a signal model (periodic signal) that may be extended in several ways to handle various forms of aperiodicity that occur in particular applications. Finally, interesting parallels may be drawn with models of auditory processing.