activity
20172026
most citedFaSNet: Low-latency Adaptive Beamforming for Multi-microphone Audio Processing

30 citations · 66 across the 37 of their papers we have counts for

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10 papers · 1 filter

cs.SD2026

Cocktail-Talker: Multi-Speaker Dialog Modeling in Noisy Social Environments with Turn Action GRPO

Xilin Jiang, Riki Shimizu, Sukru Samet Dindar +3

Spoken dialog systems are typically designed for clean, dyadic interactions in which a single user and an assistant take turns speaking. Real-world social conversations, however, a…

cs.SD2026

Sympatheia: Emotionally Adaptive Voice Assistant with Continuous Affect Conditioning

Sukru Samet Dindar, Riki Shimizu, Xilin Jiang +1

Empathetic spoken dialogue systems must infer a user's emotional state to respond appropriately, yet everyday speech often carries weak, neutral, or ambiguous affective cues. To ad…

cs.SD2026

AVMeme Exam: A Multimodal Multilingual Multicultural Benchmark for LLMs' Contextual and Cultural Knowledge and Thinking

Xilin Jiang, Qiaolin Wang, Junkai Wu +30

Internet audio-visual clips convey meaning through time-varying sound and motion, which extend beyond what text alone can represent. To examine whether AI models can understand suc…

cs.SD2025

SightSound-R1: Cross-Modal Reasoning Distillation from Vision to Audio Language Models

Qiaolin Wang, Xilin Jiang, Linyang He +2

While large audio-language models (LALMs) have demonstrated state-of-the-art audio understanding, their reasoning capability in complex soundscapes still falls behind large vision-…

cs.SD2025

ZeroSep: Separate Anything in Audio with Zero Training

Chao Huang, Yuesheng Ma, Junxuan Huang +6

Audio source separation is fundamental for machines to understand complex acoustic environments and underpins numerous audio applications. Current supervised deep learning approach…

cs.SD2025

Bridging Ears and Eyes: Analyzing Audio and Visual Large Language Models to Humans in Visible Sound Recognition and Reducing Their Sensory Gap via Cross-Modal Distillation

Xilin Jiang, Junkai Wu, Vishal Choudhari +1

Audio large language models (LLMs) are considered experts at recognizing sound objects, yet their performance relative to LLMs in other sensory modalities, such as visual or audio-…