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.
arXiv:2606. 19325v1 Announce Type: cross Abstract: Existing multi-speaker dialogue systems bind speakers to utterances through structured supervision: per-turn tags, multi-stream transcriptions, or learnable speaker embeddings.
By Michael Finkelson, Daniel Segal, Eitan Richardson, Shahar Armon, Nani Goldring, Poriya Panet, Nir Zabari, Benjamin Brazowski, Or Patashnik, Yoav HaCohen
SsgCaps is a publicly available dataset of human-engineered sound scenes, each paired with a precisely structured prompt that guides the sampling process. The prompts are drawn from a predefined action-based typology, enabling extensive yet plausible sampling. A comparative quantitative analysis shows only small differences between the open and private versions, supporting the recommendation of the open version for benchmarking sound scene generation algorithms.
By Modan Tailleur (LS2N), Junwon Lee (LS2N), Laurie M Heller (LS2N), Mathieu Lagrange (LS2N), Keunwoo Choi, Brian McFee, Keisuke Imoto, Yuki Okamoto
Existing multi-speaker dialogue systems bind speakers to utterances through structured supervision: per-turn tags, multi-stream transcriptions, or learnable speaker embeddings. These systems operate within speech-only pipelines that produce clean vocal sequences without the ambient texture of real conversations.
The paper introduces an evaluation framework for structured audio captions that separates acoustic and semantic aspects, such as timestamped sound event descriptions. It covers five axes—tag sets, descriptions, reasoning, numeric measurements, and spectral profiles—using large language model judges for semantics and deterministic metrics for temporal and acoustic features. Controlled perturbations validate that the metrics are robust to paraphrases but sensitive to real semantic and acoustic errors.
By Liang-Yuan Wu, Sripathi Sridhar, Mark Cartwright, Magdalena Fuentes
The paper introduces STAG, a post‑hoc framework that provides token‑level spectro‑temporal grounding for captions produced by audio‑based multimodal large language models (MLLMs). STAG estimates temporal support for each token via vocabulary projections of encoded audio, measures frequency‑band relevance through controlled spectral occlusion, and fuses these signals into a spectro‑temporal relevance map. Evaluations across ten explanation methods and four grounding benchmarks show that STAG achieves superior event‑localization performance on every dataset, and counterfactual deletion experiments confirm that removing the identified evidence selectively reduces model confidence and often eliminates the corresponding event from regenerated captions.
By Lucia Cascone, Valeria Fraenza, Michele Nappi, Fabio Narducci, Benedetto Simone
arXiv:2609.22452v1 Announce Type: new
Abstract: Long-context understanding remains a fundamental challenge for large language models, as excessively long inputs often lead models to forget salient in...
By Xize Cheng, Wenxu Jia, Chenyuhao Wen, Dongjie Fu, Zehan Wang, Xinyu Zhang, Tao Jin
arXiv:2608.22236v2 Announce Type: replace-cross
Abstract: Large audio-language models (LALMs) have shown promising progress in understanding speech, music, and general sound events, yet their ability...
By Yize Li, Ningyuan Yang, Sile Yin, Sindhuja Thogarrati, Sung-En Chang, Andrew C. Singer, Xue Lin, Chuan-Che Huang, Shuo Zhang
arXiv:2610.07338v1 Announce Type: cross
Abstract: Audio benchmarks are built around short, pre-segmented clips, limiting model design to brief inputs or fixed vocabularies. To close this gap, we intr...
By Kwanghee Choi, Suwon Shon, Dmitriy Serdyuk, Guitang Lan, Chao-Wei Huang, Mohammad Sadegh Rasooli, Sangeeta Srivastava, Zhaojiang Lin, Saurabh Adya, Ming Sun
arXiv:2607. 13408v1 Announce Type: cross Abstract: Recent text-to-audio models generate high-quality audio, but often fail to follow instructions involving multiple sound events and temporal order.
By Chun-Yi Kuan, Siwon Kim, Byeonggeun Kim, Suyoun Kim, Bo-Ru Lu, Qinming Tang, Ankur Gandhe, Hung-yi Lee, Chieh-Chi Kao, Chao Wang
arXiv:2609.33212v2 Announce Type: replace
Abstract: Speaker verification systems achieve high accuracy but provide little account of the acoustic evidence behind their judgments. Making these systems...
By Massa Baali, Sarthak Bisht, Ziyue Qiu, Joseph Konan, Rita Singh, Bhiksha Raj
arXiv:2608.29120v1 Announce Type: cross
Abstract: Speech Language Models (SLMs) are increasingly deployed in multi-speaker environments, yet their ability to attribute speech to the correct speaker a...
By Dongwook Lee, Sangkwon Park, Eunwoo Song, Che Hyun Lee, Youngho Cho, Junho Kim, June Young Yi, Heeseung Kim, Sungroh Yoon