Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.16217

RefTAG: Reflection-aware Temporal Audio Grounding in Large Audio Language Models

Yuhang Dai·Xin Shu·Zhennan Lin·Mingchen Shao·Chengyou Wang·Xiangang Li·Jianwei Yu·Lei Xie
View original source ↗Read source document ↗Export metadata ↓

Abstract

Temporal audio grounding requires locating every occurrence of a queried target, making both occurrence counts and interval boundaries essential. We propose RefTAG, a post-training framework for verifiable temporal self-correction. Its Reflection Chain-of-Thought (Re-CoT) protocol combines draft intervals, discrete count and boundary actions, and revised intervals in one response. Supervised fine-tuning on these trajectories (Re-CoT-SFT) teaches the protocol; reinforcement learning (RL) jointly rewards final localization and revision outcomes. Across 8,228 queries, Re-CoT-SFT improves query-weighted mIoU over final-answer SFT by 1.74 percentage points. RL further improves TAG-Bench multi-target mIoU by 0.40 points and R@0.5 by 0.86 points. The final model achieves 38.50% TAG-Bench mIoU and 51.01% mean mIoU across three datasets, outperforming the evaluated open-source MOSS-Audio and FireRedAudio models and the commercial Gemini-3.1-Pro on both measures. These results demonstrate that explicitly coupling count- and boundary-aware revision with joint reward optimization provides an effective and verifiable path to temporal audio grounding.

Explore related subjects

eess.AS

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yuhang Dai, Xin Shu, Zhennan Lin, Mingchen Shao, Chengyou Wang, Xiangang Li, Jianwei Yu, Lei Xie. 2026-10-12. RefTAG: Reflection-aware Temporal Audio Grounding in Large Audio Language Models. https://arxiv.org/abs/2610.16217

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Uncertainty Quantification and Risk Control for Multiple Sound Source Localization

Reliable Sound Source Localization (SSL) plays an essential role in many downstream tasks, where informed decision making depends not only on accurate localization but also on the confidence in each estimate. This need for reliability becomes even more pronounced in challenging conditions, such as reverberant environments and multi-source scenarios. However, existing SSL methods typically provide only point estimates, offering limited or no Uncertainty Quantification (UQ). We leverage the Conformal Prediction (CP) framework and its extensions for controlling general risk functions to develop two complementary UQ approaches for SSL. The first assumes that the number of active sources is known and constructs prediction regions that cover the true source locations. The second addresses the more challenging setting where the source count is unknown, first reliably estimating the number of active sources and then forming corresponding prediction regions. We evaluate the proposed methods on extensive simulations and real-world recordings across varying reverberation levels and source configurations. Results demonstrate reliable finite-sample guarantees and consistent performance for both known and unknown source-count scenarios, highlighting the practical utility of the proposed frameworks for uncertainty-aware SSL.

eess.AS↗
arXiv2026

DELTA-TTS: Adapting Autoregressive Model into Diffusion Language Model for Text-to-Speech

Autoregressive (AR) text-to-speech (TTS) models generate discrete speech tokens sequentially, which makes inference slow and can degrade robustness, since local errors propagate to later positions and can escalate into hallucination. This limitation stems from their left-to-right AR commitment: each token must be determined before future speech-token context is available. However, such ordering is not an inherent requirement for TTS, since the model receives the full input text before synthesis. In this paper, we introduce DELTA-TTS, a lightweight LoRA-based adaptation framework that converts a pretrained AR TTS model into a discrete diffusion language model (dLLM) for confidence-ordered speech-token decoding. To better capture the local structure of speech, DELTA-TTS incorporates a convolution module that injects local acoustic context, together with a 1/t-weighted training objective and a time-shifted inference schedule that together defer low-confidence positions to later steps. Trained on only 585 hours of LibriTTS, DELTA-TTS achieves a 1.75% WER on Seed-TTS test-en, outperforming its AR backbone while generating tokens 3.3 times faster. Further analysis shows that DELTA-TTS produces sharper text-speech alignment, increases overall decoding confidence, and mitigates the hallucinations observed in AR generation. Audio samples, inference code, and model weights are available at https://slpr-lab.github.io/DELTA-TTS.

eess.AS↗
arXiv2026

Modeling and Design of a Diaphragm-Integrated Photonic Waveguide Microphone

Can an integrated optical readout improve the useful operating range of a miniature microphone? We address this question with an analytical model of a diaphragm carrying the sensing arm of a Mach-Zehnder interferometer (MZI). The model links pressure-induced bending to waveguide elongation and then to photocurrent, allowing mechanical sensitivity and optical detection limits to be assessed separately. Optical shot noise and back-cavity thermomechanical noise set the pressure resolution, while a 10% total harmonic distortion criterion defines acoustic overload. Two operating regimes are examined: a compact MEMS microphone with 1 mW optical input and a measurement microphone with 100 mW optical input. For the compact case, a 1 mm diameter, 1 micrometer thick, stress-free silicon diaphragm and 100% modulation efficiency give a predicted signal-to-noise ratio of 78 dB and acoustic overload pressure of 124 dB. The higher-power case lowers the modeled noise floor and increases dynamic range relative to the selected measurement microphone benchmark. These estimates rely on favorable assumptions and do not establish a substantial overall advantage in conventional microphone applications. Remote optical interrogation offers a separate motivation for harsh-environment sensing, but the temperature tolerance of the proposed sensing head remains unverified. The analysis identifies the design tradeoffs and measurements needed to test the predicted performance.

eess.AS↗