Semantic visually-guided acoustic highlighting with large vision-language models

2026-01-12

AI Summary

This work proposes an automated, cinematic audio remixing approach grounded in large vision-language models to overcome the limitations of current manual workflows, which struggle to leverage video semantics for effective audio-visual alignment. By extracting six categories of visual semantic features—including shot focus, color tone, and scene context—as guidance signals for audio remixing, the study systematically evaluates their impact on perceived audio quality. Experimental results demonstrate that shot focus, color tone, and scene context significantly enhance perceptual quality, outperforming state-of-the-art methods. This research represents the first systematic identification of the most effective visual semantic cues for audio remixing, offering a novel pathway toward lightweight, multimodal automated mixing systems.

Technology Category

• Computer Vision: Language and Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Balancing dialogue, music, and sound effects with accompanying video is crucial for immersive storytelling, yet current audio mixing workflows remain largely manual and labor-intensive. While recent advancements have introduced the visually guided acoustic highlighting task, which implicitly rebalances audio sources using multimodal guidance, it remains unclear which visual aspects are most effective as conditioning signals.We address this gap through a systematic study of whether deep video understanding improves audio remixing. Using textual descriptions as a proxy for visual analysis, we prompt large vision-language models to extract six types of visual-semantic aspects, including object and character appearance, emotion, camera focus, tone, scene background, and inferred sound-related cues. Through extensive experiments, camera focus, tone, and scene background consistently yield the largest improvements in perceptual mix quality over state-of-the-art baselines. Our findings (i) identify which visual-semantic cues most strongly support coherent and visually aligned audio remixing, and (ii) outline a practical path toward automating cinema-grade sound design using lightweight guidance derived from large vision-language models.

Problem

• visually-guided audio remixing
• audio mixing automation
• vision-language models
• multimodal guidance
• semantic audio-visual alignment

Innovation

• visually-guided audio mixing
• large vision-language models
• semantic audio highlighting
• multimodal sound design
• video-to-audio alignment