AI-Based Sound Effect Generation: A Narrative Review of Generative Models Across Input Modalities
A narrative review published on arXiv on 2026-08-04 examines 30 peer-reviewed articles on AI-based generative models for sound effect synthesis, focusing on text, visual, audio, and multimodal inputs. The review covers the past five years and finds that multiple models achieve state-of-the-art performance in high-fidelity, semantically aligned, and temporally coherent sound effect generation, while noting persistent challenges in temporal synchronization for complex scenes.
A review of 30 peer-reviewed articles on AI-driven sound effect generation, published on arXiv, analyzes how input modalities (text, visual, audio, multimodal) influence output quality, controllability, and contextual relevance. It reports that recent generative models achieve high-fidelity, semantically aligned, and increasingly temporally coherent sound effects, but highlights ongoing difficulties with temporal synchronization in complex scenarios.
The review indicates that multimodal approaches are advancing temporal coherence, but synchronization for complex, dynamic scenes remains a key technical bottleneck. Future progress may depend on better alignment mechanisms between visual events and audio generation.
The findings suggest that AI sound effect tools are approaching production-ready quality for simple use cases, but adoption in high-precision media (film, games) may be slowed by synchronization limitations. Companies developing such tools may need to focus on temporal accuracy to capture professional markets.
AI-generated sound effects could reduce costs and time in media production, enabling rapid prototyping and personalized audio experiences. However, the current synchronization challenges may limit immediate ROI in high-end professional settings, making simpler applications (e.g., user-generated content, indie games) the near-term value drivers.
Observable next signals include research on improved temporal alignment techniques, potential integration of these models into creative software suites, and industry benchmarks for synchronization accuracy. Watch for startups or established audio companies releasing beta tools targeting post-production workflows.