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AI generative models for sound effect synthesis reviewed

By PulseAugur Editorial · [1 sources] ·

A recent review paper published on arXiv examines the advancements in AI-based generative models for sound effect synthesis. The paper analyzes 30 peer-reviewed articles from the past five years, focusing on how different input modalities like text, visuals, and audio influence the quality and relevance of generated sound effects. While current models demonstrate high fidelity and semantic alignment, challenges remain in temporal synchronization for complex scenarios and bridging the gap between objective metrics and human perception. AI

IMPACT This review highlights progress in AI-driven sound design, suggesting future workflows will become more adaptive and context-aware.

RANK_REASON The cluster contains a peer-reviewed academic paper detailing research findings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI generative models for sound effect synthesis reviewed

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The cluster contains a peer-reviewed academic paper detailing research findings. [lever_c_demoted from research: ic=1 ai=1.0]
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52 days old
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Sandy Abdo, Bill Kapralos, Priyamvada Tripathi, KC Collins, Adam Dubrowski ·

    AI-Based Sound Effect Generation: A Narrative Review of Generative Models Across Input Modalities

    arXiv:2608.03742v1 Announce Type: cross Abstract: Sound effects play a crucial role in conveying actions, events, and environmental cues across digital applications, often requiring a high degree of variation and contextual adaptability. Artificial intelligence (AI)-driven audio …

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