2 papers
cs.SD2026
AR&D: A Framework for Retrieving and Describing Concepts for Interpreting AudioLLMs
Townim Faisal Chowdhury, Ta Duc Huy, Siqi Pan +2
Despite strong performance in audio perception tasks, large audio-language models (AudioLLMs) remain opaque to interpretation. A major factor behind this lack of interpretability i…
eess.AS2025
Blind Estimation of Sub-band Acoustic Parameters from Ambisonics Recordings using Spectro-Spatial Covariance Features
Hanyu Meng, Jeroen Breebaart, Jeremy Stoddard +2
Estimating frequency-varying acoustic parameters is essential for enhancing immersive perception in realistic spatial audio creation. In this paper, we propose a unified framework…