Segmented comparisons
SA+ provides several improvements to the ‘segmented comparisons’ window. First, it is
now very easy to move (slide) sounds in reference to each other. Clicking just below the
sound moves the onset to the mouse position. Double clicking below the sound shifts to
‘sticky mouse’ mode, allowing continuous sliding of the sound. Double clicking again
below the sound will release the image.
The next feature you may find useful is auto-alignment: outline an interval
in the top sound and then in the bottom sound. You will see that the two
outlined intervals have moved to provide perfect alignment. This works
nicely using the ‘auto segment a single syllable’ mode (you can set it in
either ‘sound 1’ or ‘sound 2’ window). You can then open two song bouts
and align syllables to each other in a single click while monitoring changes
in the ‘distribution similarity’ score.
Feature distribution scores: based on the MAD table of syllables (see
Options -> Feature Normalization -> Normalization of syllable features)
we can calculate a distance score, just as we did at the level of frames.
When outlining arbitrary intervals of sound, SA+ scores the similarity
between them as if they were syllables, which is not very meaningful as a p-value, but still, it can be used as a non-metric score (just as human score
is). To obtain a more generic statistical distance estimate, in a way that is not related to
any assumption about the distribution of syllables (that is, without any normalization),
the Kolmogorov Smironov statistic should be used.
Kolmogorov-Smirnov statistic: is simply the distance between two cumulative
histograms. For each given interval, we can calculate the cumulative histogram of feature
distribution and compare it to the cumulative distribution of the same feature in the other
interval. KS values are unit-less and additive across features. The KS statistic is simple,
valid for all species and has very little assumptions in it (in contrast to all other methods
we use to score similarity). It does have one major disadvantage, which is – it uses the
interval chosen to estimate some ‘momentary’ distribution of features, but this estimate is
not robust for short intervals because in a time series of feature values, each feature value
is strongly correlated with other feature values (those that occur just prior to, or after it)
and this problem cannot be solved by reducing the overlap between features. Therefore,
the KS statistic is only meaningful statistically when the two intervals chosen include
several syllables. In this case too, you can use the KS statistic between syllables as a non-metric estimate.
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