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20242026
most citedAudioProtoPNet: An interpretable deep learning model for bird sound classification

3 citations · 3 across the 3 of their papers we have counts for

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5 papers · 1 filter

cs.SD2026

BEANS-Next and ROOTS: Broadening Audio-Language Capabilities for Bioacoustics

Christos Plachouras, David Robinson, Marius Miron +17

Bioacoustics and ethology encompass a wide range of audio understanding tasks, many of which stand to benefit from recent advances in large audio-language models. However, progress…

cs.SD2026

Audio-to-Image Bird Species Retrieval without Audio-Image Pairs via Text Distillation

Ilyass Moummad, Marius Miron, Lukas Rauch +5

Audio-to-image retrieval offers an interpretable alternative to audio-only classification for bioacoustic species recognition, but learning aligned audio-image representations is c…

cs.SD2026

BAT: Better Audio Transformer Guided by Convex Gated Probing

Houtan Ghaffari, Lukas Rauch, Christoph Scholz +1

Probing is widely adopted in computer vision to faithfully evaluate self-supervised learning (SSL) embeddings, as finetuning may misrepresent their inherent quality. In contrast, a…

cs.SD2025

Uncertainty Calibration of Multi-Label Bird Sound Classifiers

Raphael Schwinger, Ben McEwen, Vincent S. Kather +3

Passive acoustic monitoring enables large-scale biodiversity assessment, but reliable classification of bioacoustic sounds requires not only high accuracy but also well-calibrated…

cs.SD2025

Foundation Models for Bioacoustics -- a Comparative Review

Raphael Schwinger, Paria Vali Zadeh, Lukas Rauch +4

Automated bioacoustic analysis is essential for biodiversity monitoring and conservation, requiring advanced deep learning models that can adapt to diverse bioacoustic tasks. This…