6 citations · 13 across the 15 of their papers we have counts for
9 papers · 1 filter
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…
Multi-layer attentive probing improves transfer of audio representations for bioacoustics
Marius Miron, David Robinson, Masato Hagiwara +15
Probing heads map the representations learned from audio by a machine learning model to downstream task labels and are a key component in evaluating representation learning. Most b…
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…
An Entropy-Guided Curriculum Learning Strategy for Data-Efficient Acoustic Scene Classification under Domain Shift
Peihong Zhang, Yuxuan Liu, Zhixin Li +4
Acoustic Scene Classification (ASC) faces challenges in generalizing across recording devices, particularly when labeled data is limited. The DCASE 2024 Challenge Task 1 highlights…
AVEX: What Matters for Animal Vocalization Encoding
Marius Miron, David Robinson, Milad Alizadeh +14
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…
Robust detection of overlapping bioacoustic sound events
Louis Mahon, Benjamin Hoffman, Logan James +7
We propose a method for accurately detecting bioacoustic sound events that is robust to overlapping events, a common issue in domains such as ethology, ecology and conservation. Wh…
Synthetic data enables context-aware bioacoustic sound event detection
Benjamin Hoffman, David Robinson, Marius Miron +8
We propose a methodology for training foundation models that enhances their in-context learning capabilities within the domain of bioacoustic signal processing. We use syntheticall…
NatureLM-audio: an Audio-Language Foundation Model for Bioacoustics
David Robinson, Marius Miron, Masato Hagiwara +6
Large language models (LLMs) prompted with text and audio have achieved state-of-the-art performance across various auditory tasks, including speech, music, and general audio, show…
Biodenoising: Animal Vocalization Denoising without Access to Clean Data
Marius Miron, Sara Keen, Jen-Yu Liu +5
Animal vocalization denoising is a task similar to human speech enhancement, which is relatively well-studied. In contrast to the latter, it comprises a higher diversity of sound p…