10.5061/DRYAD.F4QRFJ6SD
Priyadarshani, Nirosha
0000-0002-4302-5262
Victoria University of Wellington
Marsland, Stephen
0000-0002-9568-848X
Victoria University of Wellington
Juodakis, Julius
0000-0001-5600-974X
Victoria University of Wellington
Castro, Isabel
0000-0002-3335-2024
Massey University
Listanti, Virginia
0000-0003-0555-9471
Victoria University of Wellington
Wavelet filters for automated recognition of birdsong in long-time field
recordings
Dryad
dataset
2020
Wavelets
AviaNZ
Brown kiwi (Apteryx mantelli)
Morepork (Ninox novaeseelandiae)
Kakapo (Strigops habroptilus)
Fantail (Rhipidura fuliginosa)
Saddleback (Philesturnus rufusater)
Royal Society of New Zealand Te Aparangi
17-MAU-154
Te Punaha Matatini - New Zealand Centre of Research Excellence in
Complex Systems
Kiwi Recovery Group, New Zealand Department of Conservation
National Science Challenge on Science for Technological Innovation
2020-01-23T00:00:00Z
2020-01-23T00:00:00Z
en
6789313989 bytes
3
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
1. Ecoacoustics has the potential to provide a large amount of information
about the abundance of many animal species at a relatively low cost.
Acoustic recording units are widely used in field data collection, but the
facilities to reliably process the data recorded -- recognising calls that
are relatively infrequent, and often significantly degraded by noise and
distance to the microphone -- are not well developed yet. 2. We propose a
call detection method for continuous field recordings that can be trained
quickly and easily on new species, and degrades gracefully with increased
noise or distance from the microphone. The method is based on the
reconstruction of the sound from a subset of the wavelet nodes (elements
in the wavelet packet decomposition tree). It is intended as a
preprocessing filter, therefore we aim to minimise false negatives: false
positives can be removed in subsequent processing, but missed calls will
not be looked at again. 3. We compare our method to standard call
detection methods, and also to machine learning methods (using as input
features either wavelet energies or Mel-Frequency Cepstral Coefficients
(MFCC)) on real-world noisy field recordings of six bird species. The
results show that our method has higher recall (proportion detected) than
the alternative methods: 87% with 85% specificity on >53 hrs of
test data, resulting in an 80% reduction in the amount of data that needed
further verification. It detected >60% of calls that were extremely
faint (far away), even with high background noise. 4. This preprocessing
method is available in our AviaNZ bioacoustic analysis program and enables
the user to significantly reduce the amount of subsequent processing
required (whether manual or automatic) to analyse continuous field
recordings collected by spatially and temporally large-scale monitoring of
animal species. It can be trained to recognise new species without
difficulty, and if several species are sought simultaneously, filters can
be run in parallel.
These are the annotated acoustic datasets of five species used in the
paper: brown kiwi (Apteryx mantelli), morepork (Ninox novaeseelandiae),
kakapo (Strigops habroptilus), fantail (Rhipidura fuliginosa), and
saddleback (Philesturnus rufusater). Each dataset includes the sound
(.wav) and annotation files (.data; AviaNZ format). The recordings were
mainly collected using omnidirectional acoustic recorders from the
New Zealand Department of Conservation (DOC) while some recordings were
made using SM2 recorders. The data was collected from variety of locations
accorss New Zealand.