6 citations · 13 across the 15 of their papers we have counts for
16 papers
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…
Beyond task performance: Decoding bioacoustic embeddings with speech features
Ines Nolasco, Jules Cauzinille, Marius Miron +8
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. This hinders…
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…
Beyond the Baseband: Adaptive Multi-Band Encoding for Full-Spectrum Bioacoustics Classification
Eklavya Sarkar, Marius Miron, David Robinson +6
Animals hear and vocalize across frequency ranges that differ substantially from humans, often extending into the ultrasonic domain. Yet most computational bioacoustics systems rel…
BAGEL: Benchmarking Animal Knowledge Expertise in Language Models
Jiacheng Shen, Masato Hagiwara, Milad Alizadeh +9
Large language models have shown strong performance on broad-domain knowledge and reasoning benchmarks, but it remains unclear how well language models handle specialized animal-re…
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…
Compact Hypercube Embeddings for Fast Text-based Wildlife Observation Retrieval
Ilyass Moummad, Marius Miron, David Robinson +7
Large-scale biodiversity monitoring platforms increasingly rely on multimodal wildlife observations. While recent foundation models enable rich semantic representations across visi…
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…
Crossing the Species Divide: Transfer Learning from Speech to Animal Sounds
Jules Cauzinille, Marius Miron, Olivier Pietquin +4
Self-supervised speech models have demonstrated impressive performance in speech processing, but their effectiveness on non-speech data remains underexplored. We study the transfer…
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…
Countering Reward Over-optimization in LLM with Demonstration-Guided Reinforcement Learning
Mathieu Rita, Florian Strub, Rahma Chaabouni +3
While Reinforcement Learning (RL) has been proven essential for tuning large language models (LLMs), it can lead to reward over-optimization (ROO). Existing approaches address ROO…
Language Evolution with Deep Learning
Mathieu Rita, Paul Michel, Rahma Chaabouni +3
Computational modeling plays an essential role in the study of language emergence. It aims to simulate the conditions and learning processes that could trigger the emergence of a s…