Computer Comparison by Dynamic Time Warping
A common task for bioacoustic studies is to
compare two signals. The underlying question may be “are
these two signals the same or are they different?”
Alternatively, it may be more general: “how different are
these two signals?” Such questions can be successfully
addressed through visual inspection of spectrographs,
especially using a careful experimental design. However, if
a large number of signals are to be compared, visual
inspection can require an unmanageable length of time, plus
keeping your visual comparisons accurately calibrated is
more and more difficult.
Computer-based comparisons of acoustic signals offer the
promise of some impressive benefits: the algorithm will not
change its assessment over time, it is likely to be much
faster than human comparison, and if the algorithm can be
accurately described, so can the basis for the comparison.
Moreover, as more is discovered about acoustic perception,
there is the possibility that algorithms can be tuned using
biologically based parameters. However, at the moment such
detailed tuning is not really possible, and the first
downside of computer-based algorithms is that they reflect
the program written by a human being – there is not
necessarily a strong link between the comparison algorithm
and what organisms actually do. Secondly, anecdotal
evidence suggests that no computer algorithm has reached
the sophistication of the human visual system for comparing
two signals. All of this is to say: computer-based
comparisons are no panacea (at least not yet!).
Nevertheless, they are increasingly becoming a valuable
weapon in a bioacoustician’s arsenal. Luscinia has an
implementation of a fairly advanced comparison algorithm
for comparing individual elements, dynamic time warping.
It also integrates element comparisons for comparisons
of larger units of sound (e.g. songs). Finally,
Luscinia incorporates some statistical analysis tools,
such as generating UPGMA trees and NMDS graphs of the
distances between signals to visualize results.
Having specified a comparison scheme,
carrying out a computational comparison in Luscinia
involves three steps:
1) Select the acoustic parameters that you wish Luscinia to
use, and how they are weighted.
2) Decide which hierarchical levels of
organization (element, syllable, syllable
transition, and song) you wish to carry out
comparisons at.
3) Decide what types of statistical analysis of the results
of the comparisons you wish to carry out.