Tootfinder

Ecologically Valid Benchmarking and Adaptive Attention: Scalable Marine Bioacoustic Monitoring
Nicholas R. Rasmussen, Rodrigue Rizk, Longwei Wang, KC Santosh
https://arxiv.org/abs/2509.04682

Ecologically Valid Benchmarking and Adaptive Attention: Scalable Marine Bioacoustic Monitoring
Underwater Passive Acoustic Monitoring (UPAM) provides rich spatiotemporal data for long-term ecological analysis, but intrinsic noise and complex signal dependencies hinder model stability and generalization. Multilayered windowing has improved target sound localization, yet variability from shifting ambient noise, diverse propagation effects, and mixed biological and anthropogenic sources demands robust architectures and rigorous evaluation. We introduce GetNetUPAM, a hierarchical nested cros…

Crosslisted article(s) found for cs.IR. https://arxiv.org/list/cs.IR/new
[1/1]:
- Ecologically Valid Benchmarking and Adaptive Attention: Scalable Marine Bioacoustic Monitoring
Nicholas R. Rasmussen, Rodrigue Rizk, Longwei Wang, KC Santosh

Crossing the Species Divide: Transfer Learning from Speech to Animal Sounds
Jules Cauzinille, Marius Miron, Olivier Pietquin, Masato Hagiwara, Ricard Marxer, Arnaud Rey, Benoit Favre
https://arxiv.org/abs/2509.04166

Crossing the Species Divide: Transfer Learning from Speech to Animal Sounds
Self-supervised speech models have demonstrated impressive performance in speech processing, but their effectiveness on non-speech data remains underexplored. We study the transfer learning capabilities of such models on bioacoustic detection and classification tasks. We show that models such as HuBERT, WavLM, and XEUS can generate rich latent representations of animal sounds across taxa. We analyze the models properties with linear probing on time-averaged representations. We then extend the a…

Replaced article(s) found for cs.SD. https://arxiv.org/list/cs.SD/new
[1/1]:
- Synthetic data enables context-aware bioacoustic sound event detection
Benjamin Hoffman, et al.

Towards High-Fidelity and Controllable Bioacoustic Generation via Enhanced Diffusion Learning
Tianyu Song, Ton Viet Ta
https://arxiv.org/abs/2509.00318 https://

Towards High-Fidelity and Controllable Bioacoustic Generation via Enhanced Diffusion Learning
Generative modeling offers new opportunities for bioacoustics, enabling the synthesis of realistic animal vocalizations that could support biomonitoring efforts and supplement scarce data for endangered species. However, directly generating bird call waveforms from noisy field recordings remains a major challenge. We propose BirdDiff, a generative framework designed to synthesize bird calls from a noisy dataset of 12 wild bird species. The model incorporates a "zeroth layer" stage for multi-s…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[5/5]:
- What Matters for Bioacoustic Encoding
Marius Miron, et al.
https://

Robust Bioacoustic Detection via Richly Labelled Synthetic Soundscape Augmentation
Kaspar Soltero, Tadeu Siqueira, Stefanie Gutschmidt
https://arxiv.org/abs/2507.16235

Robust Bioacoustic Detection via Richly Labelled Synthetic Soundscape Augmentation
Passive Acoustic Monitoring (PAM) analysis is often hindered by the intensive manual effort needed to create labelled training data. This study introduces a synthetic data framework to generate large volumes of richly labelled training data from very limited source material, improving the robustness of bioacoustic detection models. Our framework synthesises realistic soundscapes by combining clean background noise with isolated target vocalisations (little owl), automatically generating dynamic…

What Matters for Bioacoustic Encoding
Marius Miron, David Robinson, Milad Alizadeh, Ellen Gilsenan-McMahon, Gagan Narula, Olivier Pietquin, Matthieu Geist, Emmanuel Chemla, Maddie Cusimano, Felix Effenberger, Masato Hagiwara, Benjamin Hoffman, Sara Keen, Diane Kim, Jane Lawton, Jen-Yu Liu, Aza Raskin
https://arxiv.org/abs/2508.11845…

What Matters for Bioacoustic Encoding
Bioacoustics, the study of sounds produced by living organisms, plays a vital role in conservation, biodiversity monitoring, and behavioral studies. Many tasks in this field, such as species, individual, and behavior classification and detection, are well-suited to machine learning. However, they often suffer from limited annotated data, highlighting the need for a general-purpose bioacoustic encoder capable of extracting useful representations for diverse downstream tasks. Such encoders have b…

Crosslisted article(s) found for cs.IR. https://arxiv.org/list/cs.IR/new
[1/1]:
- What Matters for Bioacoustic Encoding
Marius Miron, et al.
https://

Replaced article(s) found for cs.SD. https://arxiv.org/list/cs.SD/new
[1/1]:
- Generative AI-based data augmentation for improved bioacoustic classification in noisy environments
Anthony Gibbons, Emma King, Ian Donohue, Andrew Parnell

Distilling Spectrograms into Tokens: Fast and Lightweight Bioacoustic Classification for BirdCLEF 2025
Anthony Miyaguchi, Murilo Gustineli, Adrian Cheung
https://arxiv.org/abs/2507.08236

Distilling Spectrograms into Tokens: Fast and Lightweight Bioacoustic Classification for BirdCLEF+ 2025
The BirdCLEF+ 2025 challenge requires classifying 206 species, including birds, mammals, insects, and amphibians, from soundscape recordings under a strict 90-minute CPU-only inference deadline, making many state-of-the-art deep learning approaches impractical. To address this constraint, the DS@GT BirdCLEF team explored two strategies. First, we establish competitive baselines by optimizing pre-trained models from the Bioacoustics Model Zoo for CPU inference. Using TFLite, we achieved a nearly…

No Free Lunch from Audio Pretraining in Bioacoustics: A Benchmark Study of Embeddings
Chenggang Chen, Zhiyu Yang
https://arxiv.org/abs/2508.10230 https://a…

No Free Lunch from Audio Pretraining in Bioacoustics: A Benchmark Study of Embeddings
Bioacoustics, the study of animal sounds, offers a non-invasive method to monitor ecosystems. Extracting embeddings from audio-pretrained deep learning (DL) models without fine-tuning has become popular for obtaining bioacoustic features for tasks. However, a recent benchmark study reveals that while fine-tuned audio-pretrained VGG and transformer models achieve state-of-the-art performance in some tasks, they fail in others. This study benchmarks 11 DL models on the same tasks by reducing thei…