SAIL is proposed, a Spatial Audio Intelligence framework with LLMs that preserves acoustic-spatial structure and source-level correspondence from audio encoding to LLM alignment and achieves consistent improvements in dual-source sound event detection, direction and distance estimation, and spatial reasoning.
Zheng-Ding Luo, Jin-Yang Wu, Hao-Zhe Ma et al.· 0 citations
Simulation results demonstrate that the proposed adaptive momentum ASSS-MGDFxLMS (AMAS-MGDFxLMS) algorithm achieves faster convergence than ASSS-MGDFxLMS while maintaining stable and effective noise reduction performance.
Junwei Ji, Woon-Seng Gan, Bo-Xiang Wang et al.· 0 citations
A model-agnostic meta-learning (MAML) based initialization strategy for DMCANC that achieves substantially faster convergence and improved noise reduction performance compared with conventional DMCANC, highlighting the potential of MAML initialization as an effective method for large-scale ANC.
Xiaoyi Shen, Junwei Ji, Woon-seng Gan et al.· 0 citations
A temperature-perceptive SFANC (TP-SFANC) approach is proposed that employs a lightweight one-dimensional convolutional neural network (1D CNN) trained using a multi-task learning strategy to dynamically select the optimal control filter.
Boxiang Wang, M. Misol, Zheng-wu Luo et al.· 2 citations
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