arXiv Machine Learning

Adaptive Loss Balancing for Multi-Task Bioacoustic Classification of Bird Species and Call Types

arXiv:2607. 03304v1 Announce Type: cross Abstract: Reliable analysis of bird vocalisations in passive acoustic monitoring requires models handling multiple, imbalanced annotation targets.

arXiv AI

Jun 6

MAviS: A Multimodal Conversational Assistant For Avian Species

arXiv:2603. 07294v2 Announce Type: replace-cross Abstract: Fine-grained understanding and species-specific multimodal question answering are vital for advancing biodiversity conservation and ecological monitoring.

By Yevheniia Kryklyvets, Mohammed Irfan Kurpath, Sahal Shaji Mullappilly, Jinxing Zhou, Fahad Shabzan Khan, Rao Anwer, Salman Khan, Hisham Cholakkal

llms nlp multimodal benchmarks

arXiv AI

Jul 17

Can Tokens Compete? Token Representations against Supervised CNN Backbones for BirdCLEF+ 2026

arXiv:2607. 14474v1 Announce Type: cross Abstract: This paper details the DS@GT ARC team's approach to BirdCLEF+ 2026, multi-label detection of animal vocalizations in soundscapes from the Pantanal wetlands.

By Anthony Miyaguchi, Murilo Gustineli, Adrian Cheung

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arXiv AI

Jun 12

Decoding Insect Song: A Multitask Semisupervised Orthoptera Bioacoustic Classifier

arXiv:2606. 13236v1 Announce Type: cross Abstract: Passive acoustic monitoring holds great promise for ecological inference, yet existing automated tools are typically narrowly trained and non-transferable.

By Olga Isupova, Danil Kuzin, Ella Browning, Tom Mills, Steven Reece

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arXiv Machine Learning

Jun 15

Beyond task performance: Decoding bioacoustic embeddings with speech features

arXiv:2606. 14662v1 Announce Type: new Abstract: Pretrained audio embeddings are standard in bioacoustics, yet little is known about which acoustic features these models encode, nor which are useful for a given task.

By Ines Nolasco, Jules Cauzinille, Marius Miron, Gagan Narula, Milad Alizadeh, Emmanuel Fernandez, Matthieu Geist, Ellen Gilsenan-McMahon, Olivier Pietquin, Emmanuel Chemla, Sara Keen

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arXiv Machine Learning

Jul 16

MetaPerch: Learning from metadata for bioacoustics foundation models

arXiv:2607. 14072v1 Announce Type: new Abstract: Bioacoustic foundation models rely on large-scale citizen science platforms like Xeno-Canto for geographically and ecologically diverse data.

By Mustafa Chasmai, Vincent Dumoulin, Jenny Hamer

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arXiv AI

Jun 2

VocSim: A Training-free Benchmark for Zero-shot Content Identity in Single-source Audio

arXiv:2512. 10120v2 Announce Type: replace-cross Abstract: General-purpose audio representations aim to map acoustically variable instances of the same event to nearby points, resolving content identity in a zero-shot setting.

By Maris Basha, Anja Zai, Sabine Stoll, Richard Hahnloser

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arXiv AI

Jun 12

GetNetUPAM: Ecologically Informed Nested Cross-Validation and Noise-Robust Attention for Marine Bioacoustic Monitoring

arXiv:2509. 04682v2 Announce Type: replace-cross Abstract: Deploying reliable bioacoustic monitoring systems requires models that generalize under high-noise, low-SNR conditions and evaluation protocols that expose deployment-relevant failure modes, gaps largely unaddressed in current UPAM practice.

By Nicholas R. Rasmussen, Rodrigue Rizk, Longwei Wang, KC Santosh

benchmarks

arXiv Machine Learning

Jul 27

Phylogenetic signal in marine mammal and bird vocalizations captured by audio foundation models: the limited benefit of domain-specific pretraining

arXiv:2607. 22458v1 Announce Type: new Abstract: Do learned audio embeddings encode structure that nobody told them to encode?

By V\'ictor Rinc\'on Yepes

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arXiv Machine Learning

Jul 8

Determinantal point process sampling for bioacoustic active learning

arXiv:2607. 06063v1 Announce Type: cross Abstract: Eco-acoustic monitoring generates vast volumes of audio data, making active learning a promising approach for reducing annotation effort while efficiently training reliable biodiversity classifiers.

By Hugo Magaldi, Gabriel Dubus

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arXiv Machine Learning

Jun 29

Advancing Speaker-Based Vocal Effort Classification with WavLM and Data Augmentation in Naturalistic Non-Calibrated Speech Recordings

arXiv:2606. 27543v1 Announce Type: cross Abstract: The variations in vocal effort range (e.

By Zahra Omidi, John H. L. Hansen

multimodal benchmarks

Hugging Face Trending Papers

Jul 15

MetaPerch: Learning from metadata for bioacoustics foundation models

Bioacoustic foundation models rely on large-scale citizen science platforms like Xeno-Canto for geographically and ecologically diverse data. Recent work has shown that supervision alone can produce SotA species detection models when trained on this large-scale data -- however, there remains unutilized potential in the form of recording metadata readily available within these community-driven data hubs.

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arXiv AI

Jul 13

Parameter-Efficient Vision-Language Adaptation with Continuous Metadata Conditioning for Animal Re-Identification

arXiv:2607. 09443v1 Announce Type: cross Abstract: Long-term animal re-identification (ReID) must remain robust to gradual morphological evolution and seasonal appearance shifts.

By Anil Osman Tur, Tonje Knutsen Sordalen, Kim Tallaksen Halvorsen, Cigdem Beyan

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