https://www.discovermagazine.com/planet-earth/orangutan-language-is-more-sophisticated-than-once-thought

Orangutans have a lot to say. And the way
they do so may be more complicated and
sophisticated than previously appreciated,
according to new study in PeerJ Life &
Environment.

Orangutans, the great apes of Southeast
Asia, have a reputation for complex vocal
communication. But understanding the
nuances of their repertoire has proved
challenging for researchers.

Wendy Erb, a primatologist with the
K. Lisa Yang Center for Conservation
Bioacoustics at the Cornell Lab of
Ornithology and her team sought to
decipher “long calls” between
orangutans. Researchers believe they
use these vocalizations to
communicate over long distances in
the rainforests of Indonesia.

Their study didn’t examine what the
primates were saying. But it helped
identify how they were saying it.
The researchers concluded that
orangutans use a far greater variety
of sounds than has been previously
appreciated.
...
Both the humans and machines hit upon
the same patterns.

"We identified three distinct pulse
types that were well differentiated by
both humans and machines," Erb said in
a statement. “Orangutans may possess a
far greater repertoire of sound types
than we have described, highlighting
the complexity of their vocal system."
...


https://peerj.com/articles/17320/
Vocal complexity in the long calls of
Bornean orangutans
May 14, 2024

Abstract
Vocal complexity is central to many
evolutionary hypotheses about animal
communication. Yet, quantifying and
comparing complexity remains a
challenge, particularly when vocal
types are highly graded. Male Bornean
orangutans (Pongo pygmaeus wurmbii)
produce complex and variable “long
call” vocalizations comprising multiple
sound types that vary within and among
individuals. Previous studies described
six distinct call (or pulse) types
within these complex vocalizations, but
none quantified their discreteness or
the ability of human observers to
reliably classify them. We studied the
long calls of 13 individuals to:
(1) evaluate and quantify the reliability
of audio-visual classification by three
well-trained observers, (2) distinguish
among call types using supervised
classification and unsupervised
clustering, and (3) compare the
performance of different feature sets.
Using 46 acoustic features, we used
machine learning (i.e., support vector
machines, affinity propagation, and fuzzy
c-means) to identify call types and assess
their discreteness. We additionally used
Uniform Manifold Approximation and
Projection (UMAP) to visualize the
separation of pulses using both extracted
features and spectrogram representations.
Supervised approaches showed low
inter-observer reliability and poor
classification accuracy, indicating that
pulse types were not discrete. We propose
an updated pulse classification approach
that is highly reproducible across
observers and exhibits strong
classification accuracy using support
vector machines. Although the low number
of call types suggests long calls are
fairly simple, the continuous gradation
of sounds seems to greatly boost the
complexity of this system. This work
responds to calls for more quantitative
research to define call types and
quantify gradedness in animal vocal
systems and highlights the need for a
more comprehensive framework for
studying vocal complexity vis-à-vis
graded repertoires.

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