# What is an AMDF?

The wikipedia page for Average Magnitude Difference Function/Formula (AMDF) appears to be empty. What is an AMDF? What are AMDF's properties? What are AMDF's strengths and weaknesses, as compared to other pitch estimation methods such as autocorrelation?

• This paper comes quite handy. – jojek Mar 14 '15 at 18:51

I've never seen the word "Formula" with "AMDF". My understanding of the definition of AMDF is

$$Q_x[k,n_0] \triangleq \frac{1}{N} \sum\limits_{n=0}^{N-1} \Big| x[n+n_0] - x[n+n_0+k] \Big|$$

$$n_0$$ is the neighborhood of interest in $$x[n]$$. Note that you are summing up only non-negative terms. So $$Q_x[k,n_0] \ge 0$$. We call "$$k$$" the "lag". clearly if $$k=0$$, then $$Q_x[0,n_0]=0$$. Also, if $$x[n]$$ is periodic with period $$P$$ (and let's pretend for the moment that $$P$$ is an integer) then $$Q_x[P,n_0]=0$$ and $$Q_x[mP,n_0]=0$$ for any integer $$m$$.

Now even if $$x[n]$$ is not precisely periodic, or if the period is not precisely an integer number of samples (at the particular sampling rate you are using), we would expect $$Q_x[k,n_0] \approx 0$$ for any lag $$k$$ that is close to the period or any integer multiple of the period. In fact, if $$x[n]$$ is nearly periodic, but the period is not at an integer number of samples, we expect to be able to interpolate $$Q_x[k,n_0]$$ between integer values of $$k$$ to get an even lower minimum.

My favorite is not the AMDF but the "ASDF" (guess what the "S" stands for?)

$$Q_x[k,n_0] \triangleq \frac{1}{N} \sum\limits_{n=0}^{N-1} \big( x[n+n_0] - x[n+n_0+k] \big)^2$$

Turns out you can do calculus with that because the square function has continuous derivatives, but the absolute value function does not.

Here's another reason i like ASDF better than AMDF. If $$N$$ is very large and we play a little fast-and-loose with the limits of summation:

\begin{align} Q_x[k] & = \frac{1}{N} \left( \sum_n \big( x[n] - x[n+k] \big)^2 \right) \\ & = \frac{1}{N} \left( \sum_n (x[n])^2 + \sum_n (x[n+k])^2 - 2 \sum_n x[n] x[n+k] \right) \\ & = \frac{1}{N} \sum_n (x[n])^2 + \frac{1}{N}\sum_n (x[n+k])^2 - \frac{2}{N} \,\sum_n x[n] x[n+k] \\ & = \overline{x^2[n]} + \overline{x^2[n]} - 2\, R_x[k] \\ & = 2 \left( \overline{x^2[n]} - R_x[k] \right) \\ \end{align}

where

\begin{align} R_x[k] & \triangleq \frac{1}{N} \sum_n x[n] x[n+k] \\ & = \overline{x^2[n]} - \tfrac{1}{2} Q_x[k] \\ & = R_x - \tfrac{1}{2} Q_x[k] \\ \end{align}

is normally identified as the "autocorrelation" of $$x[n]$$.

So we expect the autocorrelation function to be an upside-down (and offset) replica of the ASDF. Wherever the autocorrelation peaks is where the ASDF (and usually also the AMDF) has a minimum.