Large audio-language models (LALMs) have achieved strong performance in general audio understanding, yet most are designed for monaural input and discard the inter-channel cues essential for spatial perception. In contrast, existing spatial audio-language models are purpose-built for spatial tasks and fail to capitaliz...
Jin-Bo Hu, Hang Su, Li-Chun Fan et al.· 0 citations
Audio anti-spoofing systems increasingly combine self-supervised learning, parameter-efficient fine-tuning, and graph-attention-based backends. However, performance gains in such systems are often entangled with concurrent changes in the backbone, fine-tuning strategy, and training protocol, making the independent cont...
Hao-Yu Wang, Jing Yang, Chen-Yu Liu et al.· 0 citations
MiDashengLM-Gen is an end-to-end framework that couples a pre-trained Large Language Model (LLM) with per-token conditional flow matching for autoregressive, variable-length mixed-audio scene generation and drastically improves speech intelligibility over existing unified models.
Xingwei Sun, Heinrich Dinkel, Gang Li et al.· 0 citations
MemoryCard is a video-memory-based augmentation framework that organizes long videos into self-contained Memory Cards, each corresponding to a distinct topic or event, and consistently improves long-video QA performance under comparable visual-token budgets.
Qing Yang, Pengcheng Huang, Xinze Li et al.· arXiv.org· 0 citations
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