Conveners
A02.01 Machine Learning in Bioacoustics: S008
- Jure Zeleznik (Acoustics Research Institute, Austrian Academy of Sciences)
- Daniel Haider (Acoustics Research Institute, Austrian Academy of Sciences)
A02.01 Machine Learning in Bioacoustics: P423
- Daniel Haider (Acoustics Research Institute, Austrian Academy of Sciences)
- Jure Zeleznik (Acoustics Research Institute, Austrian Academy of Sciences)
A02.01 Machine Learning in Bioacoustics: S252
- Daniel Haider (Acoustics Research Institute, Austrian Academy of Sciences)
- Jure Zeleznik (Acoustics Research Institute, Austrian Academy of Sciences)
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Daniel Haider (Acoustics Research Institute, Austrian Academy of Sciences)08/09/2026, 13:40A02 Bio-acoustics
We present a pipeline for extracting and analyzing the fundamental frequency (F0) contour patterns in the rumbles of African savanna elephants (Loxodonta africana). Rumble F0s can go as low as 15 Hz and are often masked by infrasonic background noise or overlap with other calls, making standard contour extraction methods perform poorly. To address this, we introduce a custom infrasonic-adapted...
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Sercan Alipek (University of Siegen)08/09/2026, 14:00A02 Bio-acoustics
Automatic detection and classification of bat echolocation calls is essential for large-scale biodiversity monitoring, yet remains challenging under real-world acoustic conditions and constrained hardware environments. This work presents a lightweight and deployable pipeline for bat call analysis that combines simple signal processing with efficient time-series machine learning. An adaptive...
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Florian Krebs (Joanneum Research Forschungsgesellschaft mbH)08/09/2026, 14:00A02 Bio-acoustics
Large-scale bioacoustic datasets are typically weakly labelled, assigning species labels to entire recordings without temporal annotations. When recordings are divided into short audio chunks for training, many chunks inherit the recording-level label despite containing no target species, introducing weak-label noise. In this work, we quantify this source of noise in the InsectSet459 dataset...
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Ignasi Nou-Plana (La Salle - URL)08/09/2026, 14:00A02 Bio-acoustics
Effective management of respiratory diseases in cattle (Bos taurus), particularly within the context of Bovine Respiratory Disease Complex (BRDC), relies on early, non-invasive monitoring strategies to safeguard animal health and welfare. Among the earliest observable indicators, vocal and respiratory-related sounds such as coughs and vocalizations provide valuable insight into physiological...
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Clara Hollomey (Univ. of Appl. Sciences St. Pรถlte)08/09/2026, 14:00A02 Bio-acoustics
We investigate whether the frame-theoretic properties of filterbanks can predict downstream neural network classification performance. Using a dataset of 3,080 African savanna elephant rumble vocalisations classified into four age groups, we train a convolutional neural network on magnitude spectrograms derived from 15 filterbank configurations spanning equivalent rectangular bandwidth (ERB),...
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Stephen Marsland (Victoria University of Wellington)08/09/2026, 14:20A02 Bio-acoustics
Automated bird call recognition systems trained on one recording environment often produce Automated bird call recognition systems trained on one recording environment often produce worse results when deployed to others due to differences in background noise and recording equipment, a problem known as domain shift. To isolate this issue we process data from two different recording sources into...
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3834. Machine Learning to Study Individual Budgerigar Vocalizations in a Social Setting (FA2026/860)Zsofia Katona (Acoustics Research Institute, OEAW)08/09/2026, 14:40A02 Bio-acoustics
Artificial intelligence holds powerful potential for studying animal vocal communication in naturalistic group environments, where manual annotation of audiovisual data is often prohibitively time-consuming. We are developing a pipeline to separate individual calls within a group of budgerigars (Melopsittacus undulatus) to investigate potential language-like structure in their vocalizations....
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Guillaume Dutilleux (NTNU/IE/IES/Acoustics)08/09/2026, 15:20A02 Bio-acoustics
In the context of the decline of the European common spadefoot toad (Pelobates fuscus), long term bioacoustic monitoring of this secretive species that vocalizes underwater is highly relevant. In this study, we present a software detector that features superior performance compared to literature [1]. For this study, hydrophone data was collected in Denmark and Poland with a different recorder...
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Guillaume Dutilleux (NTNU/IE/IES/Acoustics)08/09/2026, 15:40A02 Bio-acoustics
In Scandinavia, the Eurasian lynx (\textit{Lynx lynx}) is a hunted species whose populations must be monitored. We present a deep-learning-based software detector of E. lynx calls. It can be applied to long-term audio recordings carried out during the breeding season where the species produce loud and far-reaching calls. To our knowledge, this is the first published software detector for this...
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Vincent S. Kather (Naturalis Biodiversity Center)08/09/2026, 16:00A02 Bio-acoustics
Natural soundscapes, especially in hyper-diverse environments often contain unknown sound events. Strategies to identify unknown sound events, especially those produced by species not included in training sets have traditionally been infeasible to manually identify. Recently, the classification performance of bioacoustic deep learning models is becoming competitive with human annotators. Yet,...
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Bram Cuyx (Flanders Marine Institute (VLIZ))08/09/2026, 16:20A02 Bio-acoustics
Marine soundscapes contain valuable information about local ecosystems, making comprehensive analysis of their constituent events essential for accurately assessing ecosystem health. However, they are subject to high levels of continuous noise, hindering our ability to disentangle these events and analyse the soundscape. One potential approach to mitigating noise is the use of a...
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