1 citations · 1 across the 12 of their papers we have counts for
11 papers · 1 filter
ImpactMat: Continuous Material Estimation for Inverse Impact Sound Rendering
Hyebin Cho, Bumsoo Kim, Joon Son Chung +1
Impact sound rendering synthesizes the sound produced when a 3D object is struck, but practical renderers often rely on fixed material presets such as wood, plastic, or steel. Thes…
Uncovering Ordinal-Matching Bias in Audio-Visual LLMs
Jihoo Jung, Youngjoon Jang, Hyebin Cho +2
This work aims to improve how audio-visual large language models (AVLLMs) associate speech with the correct visible speaker in multi-speaker scenes. We find that current AVLLMs fre…
OmniSmartHome: A Multimodal Reasoning Benchmark for Smart-Home Agents
Jihoo Jung, Suho Yoo, Jeongsoo Choi +5
Smart-home assistants are expected to handle diverse, realistic requests that arise in daily life. In such interactions, users often rely on the surrounding multimodal context-poin…
Out of Sight, Still in Mind: Token Compression for Omni-LLMs
Suho Yoo, Youngjoon Jang, Hyebin Cho +1
The goal of this paper is to reduce the input token cost of Omni-modal large language models (Omni-LLMs) at inference time. Omni-LLMs reason jointly over audio, video and text, but…
See & Sniff: Learning Visuo-Olfactory Representations
Seongyu Kim, Seungwoo Lee, Hyeonggon Ryu +2
While modern multimodal models integrate vision with language, audio, or touch, olfaction remains largely unexplored due to the lack of paired visuo-olfactory data. We introduce Sm…
ProsoCodec: Prosody-Oriented Speech Codec for Voice Conversion
Jeongsoo Choi, Ji-Hoon Kim, Shujie Hu +1
Neural speech codecs efficiently compress speech and have become a foundation for speech generation, but they are typically learned as holistic representations that intertwine ling…
Acoustic Prompting via Stage-wise Modulation for Few-Shot Learning in Audio Language Models
Hyebin Cho, Jaehyuk Jang, Changick Kim +1
Audio-Language Models (ALMs) have shown remarkable success in zero-shot audio classification by aligning audio waveforms with text. Recent efforts to improve downstream performance…
Inference-Time Scaling for Joint Audio-Video Generation
Jaemin Jung, Kyeongha Rho, Inkyu Shin +1
Joint audio-video generation aims to synthesize realistic audio-video pairs that are both semantically aligned with text prompts and precisely synchronized. While existing joint au…
Who Wins the Conflict? Mechanistic Interpretability of Text Bias in Audio LLMs
Hyebin Cho, Suho Yoo, Jaehyuk Jang +2
While Audio Large Language Models (Audio LLMs) excel at multimodal understanding, they suffer from text dominance, a bias where models favor text over acoustic evidence, potentiall…
On the Nature of Attention Sink that Shapes Decoding Strategy in Omni-LLMs
Suho Yoo, Youngjoon Jang, Joon Son Chung
The goal of this paper is to strengthen the reasoning of Omnimodal Large Language Models (Omni-LLMs) at inference time, without additional training. These models jointly process vi…
LAMB: LLM-based Audio Captioning with Modality Gap Bridging via Cauchy-Schwarz Divergence
Hyeongkeun Lee, Jongmin Choi, KiHyun Nam +1
Automated Audio Captioning aims to describe the semantic content of input audio. Recent works have employed large language models (LLMs) as a text decoder to leverage their reasoni…