DR error measures: Difference between revisions
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<adv>{{adv|'''Least-squares linear error''' (here ''linear'' means "in frequency space, not pitch space") is a proposed error measure for approximations to [[delta-rational chord]]s. | <adv>{{adv|'''Least-squares linear error''' (here ''linear'' means "in frequency space, not pitch space") is a proposed error measure for approximations to [[delta-rational chord]]s. It has the advantage of not fixing a particular interval in the chord when constructing the chord of best fit. However, like any other numerical measure of concordance or error, you should take it with a grain of salt. | ||
The idea motivating least-squares linear error on a chord as an approximation to a given delta signature is the following (for simplicity, let’s talk about the fully DR case first): | The idea motivating least-squares linear error on a chord as an approximation to a given delta signature is the following (for simplicity, let’s talk about the fully DR case first): | ||
Revision as of 22:53, 10 December 2025
<adv>Least-squares linear error (here linear means "in frequency space, not pitch space") is a proposed error measure for approximations to delta-rational chords. It has the advantage of not fixing a particular interval in the chord when constructing the chord of best fit. However, like any other numerical measure of concordance or error, you should take it with a grain of salt.
The idea motivating least-squares linear error on a chord as an approximation to a given delta signature is the following (for simplicity, let’s talk about the fully DR case first):
Say we want the error of a chord 1:r1:r2:...:rn (in increasing order), with n > 1, in the linear domain as an approximation to a fully delta-rational chord with signature +δ1 +δ2 ... +δn, i.e. a chord
x : x + \delta_1 : \cdots : x + \sum_{l=1}^n \delta_l.
Minimizing the least-squares frequency-domain error by varying x gives you
x = \frac{\sum_{i=1}^n D_i }{-n + \sum_{i=1}^n f_i},
which you plug back into
\sqrt{\sum_{i=1}^n \Bigg( 1 + \frac{D_i}{x} - f_i \Bigg)^2 }
to obtain the least-squares linear error.</adv>
