AudioProtoPNet: An interpretable deep learning model for bird sound classification
Ecological Informatics,
Год журнала:
2025,
Номер
unknown, С. 103081 - 103081
Опубликована: Фев. 1, 2025
Язык: Английский
All thresholds barred: direct estimation of call density in bioacoustic data
Frontiers in Bird Science,
Год журнала:
2024,
Номер
3
Опубликована: Апрель 24, 2024
Passive
acoustic
monitoring
(PAM)
studies
generate
thousands
of
hours
audio,
which
may
be
used
to
monitor
specific
animal
populations,
conduct
broad
biodiversity
surveys,
detect
threats
such
as
poachers,
and
more.
Machine
learning
classifiers
for
species
identification
are
increasingly
being
process
the
vast
amount
audio
generated
by
bioacoustic
expediting
analysis
increasing
utility
PAM
a
management
tool.
In
common
practice,
threshold
is
applied
classifier
output
scores,
scores
above
aggregated
into
detection
count.
The
choice
produces
biased
counts
vocalizations,
subject
false
positive/negative
rates
that
vary
across
subsets
dataset.
this
work,
we
advocate
directly
estimating
call
density
:
proportion
windows
containing
target
vocalization,
regardless
score.
We
propose
validation
scheme
in
body
data
obtain,
through
Bayesian
reasoning,
probability
distributions
confidence
both
positive
negative
classes.
use
these
predict
site-level
densities,
distribution
shifts
(when
defining
characteristics
change).
These
methods
outputs
any
binary
operating
on
fixed-size
input
windows.
test
our
proposed
real-world
study
Hawaiian
birds
provide
simulation
results
leveraging
existing
fully
annotated
datasets,
demonstrating
robustness
variations
model
quality.
Язык: Английский
Counting the chorus: A bioacoustic indicator of population density
Ecological Indicators,
Год журнала:
2024,
Номер
169, С. 112930 - 112930
Опубликована: Дек. 1, 2024
Язык: Английский
HawkEars: A regional, high-performance avian acoustic classifier
Ecological Informatics,
Год журнала:
2025,
Номер
unknown, С. 103122 - 103122
Опубликована: Март 1, 2025
Язык: Английский
Impact of transfer learning methods and dataset characteristics on generalization in birdsong classification
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Май 9, 2025
Abstract
Animal
sounds
can
be
recognised
automatically
by
machine
learning,
and
this
has
an
important
role
to
play
in
biodiversity
monitoring.
Yet
despite
increasingly
impressive
capabilities,
bioacoustic
species
classifiers
still
exhibit
imbalanced
performance
across
habitats,
especially
complex
soundscapes.
In
study,
we
explore
the
effectiveness
of
transfer
learning
large-scale
bird
sound
classification
various
conditions,
including
single-
multi-label
scenarios,
different
model
architectures
such
as
CNNs
Transformers.
Our
experiments
demonstrate
that
both
finetuning
knowledge
distillation
yield
strong
performance,
with
cross-distillation
proving
particularly
effective
improving
in-domain
on
Xeno-canto
data.
However,
when
generalizing
soundscapes,
shallow
exhibits
superior
compared
distillation,
highlighting
its
robustness
constrained
nature.
study
further
investigates
how
use
multi-species
labels,
cases
where
these
are
present
but
incomplete.
We
advocate
for
more
comprehensive
labeling
practices
within
animal
community,
annotating
background
providing
temporal
details,
enhance
training
robust
classifiers.
These
findings
provide
insights
into
optimal
reuse
pretrained
models
advancing
automatic
recognition.
Язык: Английский