The paper introduces BEANS-Next, a comprehensive benchmark covering acoustic perception, biological category recognition, scene understanding, and in-context learning in bioacoustics, and shows that current audio‑language models perform poorly outside the narrow label‑centric tasks traditionally evaluated. To address this gap, the authors present ROOTS, a large‑scale training dataset enriched with curated real‑world data, behavioral and acoustic metadata, and synthetic generation, which improves performance across all BEANS‑Next task groups. Both the benchmark and dataset are released as open‑source resources to accelerate research in bioacoustics and ethology.
By Christos Plachouras, David Robinson, Marius Miron, Gagan Narula, Paul Laisn\'e, Anthony L. T. Fine, Benno Weck, Ellen Gilsenan-McMahon, Diane Kim, Laura Hay Mack, Maddie Cusimano, Sara Keen, Lukas Rauch, Benjamin Hoffman, Emmanuel Chemla, Emmanouil Benetos, Johan Pauwels, Milad Alizadeh, Matthieu Geist, Olivier Pietquin
arXiv:2502. 16584v2 Announce Type: replace-cross Abstract: Recent advancements in audio tokenization have significantly enhanced the integration of audio capabilities into large language models (LLMs).
By Liumeng Xue, Ziya Zhou, Jiahao Pan, Zixuan Li, Shuai Fan, Yinghao Ma, Sitong Cheng, Dongchao Yang, Haohan Guo, Yujia Xiao, Xinsheng Wang, Zixuan Shen, Chuanbo Zhu, Xinshen Zhang, Tianchi Liu, Ruibin Yuan, Zeyue Tian, Haohe Liu, Xingjian Du, Emmanouil Benetos, Ge Zhang, Yike Guo, Wei Xue
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.
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: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
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
arXiv:2607. 04383v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) reason fluently about sound yet struggle to localize precisely when events occur, while classical Sound Event Detection attains frame-level precision only over a closed label set.
By Zihan Zhang, Xize Cheng, Wenhao Yan, Tong Zhang, Dongjie Fu, Boyun Zhang, Yongbo He, Tao Jin
arXiv:2608.29121v1 Announce Type: new
Abstract: Open-Vocabulary Audio-Visual Semantic Segmentation (OV-AVSS) aims to perform pixel-level segmentation of sound-emitting objects from an open set of cat...
By Tianrui Hui, Shaofei Huang, Qisong Han, Yaxiong Wang, Lechao Cheng, Zhedong Zheng, Zhun Zhong, Richang Hong, Meng Wang
arXiv:2610.23745v1 Announce Type: cross
Abstract: Recent large audio language models (LALMs) have extended their capabilities beyond lexical speech understanding to tasks requiring paralinguistic and...
By Koki Ryu, Hitomi Yanaka
arXiv:2512.07571v3 Announce Type: replace
Abstract: This paper presents a simple method that allows to easily enhance textual pre-trained large language models with speech information, when fine-tune...
By Nicolas Calbucura, Jose Guillen, Valentin Barriere
Recent Large Audio Language Models (LALMs) have achieved remarkable progress in audio perceptual tasks across individual acoustic layers, including speech, sound, and music. However, existing benchmarks predominantly evaluate these layers in isolation, overlooking the complex contextual relationships that arise when multiple acoustic sources co-occur in real-world auditory scenes.
TUTTI is a new pre‑training framework for audio‑to‑score transcription that uses a large, fully synthetic multi‑instrument dataset generated by a symbolic music model. The approach trains a standard Transformer encoder‑decoder on these synthetic audio‑score pairs, producing a stronger foundational representation than single‑instrument training. When fine‑tuned on real datasets, TUTTI surpasses prior methods, achieving state‑of‑the‑art results and demonstrating strong cross‑instrument transferability.
By Jianhuai Hu, Yashan Wang, Shangda Wu, Zhancheng Guo, Shijie Liang, Wuna Meng, Chuanqi Yang, Xiaobing Li, Feng Yu, Maosong Sun