Average magnitude difference function pitch extractor

TLDR

The average magnitude difference function (AMDF) is a variation of autocorrelation that measures the absolute difference between delayed and original speech, yielding deep nulls at pitch periods and offering a simple, multiplication‑free, 16‑bit‑friendly method suitable for real‑time implementation. This paper describes a method for using the AMDF and associated decision logic to estimate the pitch period of voiced speech sounds. The authors implement an AMDF‑based pitch extractor, applying decision logic to identify pitch periods in both non‑real‑time simulation and real‑time processing. Experimental results demonstrate the extractor’s ability to accurately capture pitch contours and validate its measurement properties.

Abstract

This paper describes a method for using the average magnitude difference function (AMDF) and associated decision logic to estimate the pitch period of voiced speech sounds. The AMDF is a variation on autocorrelation analysis where, instead of correlating the input speech at various delays (where multiplications and summations are formed at each value of delay), a difference signal is formed between the delayed speech and the original and, at each delay, the absolute magnitude of the difference is taken. The difference signal is always zero at delay = π, and exhibits deep nulls at delays corresponding to the pitch period of voiced sounds. Some of the reasons the AMDF is attractive include the following. 1) It is a simple measurement which gives a good estimate of pitch contour, 2) it has no multiply operations, 3) its dynamic range characteristics are suitable for implementation on a 16-bit machine, and 4) the nature of its operations makes it suitable for implementation on a programmable processor or in special purpose hardware. The implementation of the AMDF pitch extractor (nonreal-time simulation and real-time) is described and experimental results presented to illustrate its basic measurement properties.