arXiv Machine Learning

A Self-Supervised Approach for Minimal-Annotation Hydroacoustic Data Exploration

arXiv:2607. 07733v1 Announce Type: cross Abstract: Passive hydroacoustic monitoring often generates large volumes of continuous recordings that are only partially exploited due to the cost of manual annotation.

arXiv Machine Learning

Sep 15

Large-scale bioacoustic detection using semantic segmentation: a deep learning framework applied to fin whale calls in ocean-bottom seismometer recordings

arXiv:2609.13281v1 Announce Type: cross Abstract: Ocean-bottom seismometers (OBS), originally deployed for geophysical research, continuously record low-frequency sound for months to years across bro...

By Jocelyn Japnanto, Alex A. Saoulis, Miriam Romagosa, Rita Leit\~ao, Gabrielle Arrieta, M\'onica A. Silva, Matthew Graham, Ana M. G. Ferreira

computer-vision

arXiv Machine Learning

Sep 15

MAST: Label-Efficient, Robust, and Generalizable Sound Detection for Biodiversity Monitoring via Masked Audio Pretraining and Self-Training

arXiv:2609.15221v1 Announce Type: cross Abstract: Passive acoustic monitoring can measure biodiversity at larger scales, but time--frequency annotation of animal vocalizations is expensive, site-spec...

By Tianyi Xu, Daniel Pimentel-Alarc\'on, Zuzana Bu\v{r}ivalov\'a, Claudia Sol\'is-Lemus

arXiv Machine Learning

Sep 23

Linear probing enables Ship-Radiated Noise recognition with pretrained audio embeddings

arXiv:2601.08358v2 Announce Type: replace Abstract: Even though the ocean covers the majority of the planet's surface, it remains the least explored ecosystem. As light and radio waves do not propaga...

By Hilde I. Hummel, Sandjai Bhulai, Rob D. van der Mei, Burooj Ghani

rag fine-tuning

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

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.

benchmarks

arXiv Computer Vision

Sep 15

UniqueShip: Mitigating Data Leakage in Acoustic Ship Classification Benchmark Datasets

arXiv:2609.13659v1 Announce Type: cross Abstract: Underwater Acoustic Target Recognition (UATR) of ships is well-suited for machine learning, yet its progress is hindered by the lack of large, divers...

By Connor Hashemi, Trevor Stout, Anthony Hoogs, Jason Parham

llms benchmarks

arXiv Machine Learning

4d ago

Beyond Discrimination: Calibrated Geoprior Fusion for Bioacoustic Monitoring

arXiv:2609.35863v1 Announce Type: cross Abstract: Modern bioacoustic foundation models like Perch and BirdNET can identify species with high discriminative accuracy, yet their confidence scores are o...

By Neha Sajja, Bart van Merri\"{e}nboer, Burcu Karagol Ayan, Tom Denton

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

rag

arXiv AI

Aug 25

BenthicDINO: Physics-Informed Self-Distillation for View-Invariant Side-Scan Sonar Representations

arXiv:2608.23215v1 Announce Type: cross Abstract: Automated perception in side-scan sonar (SSS) imagery is severely hindered by physical acoustic artifacts, resulting in representations that inextric...

By Taqi Hamoda, Hayat Rajani, Nuno Gracias

efficiency

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 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

rag efficiency 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

rag

Related stories