mlsanityData to 14 Oct 2026

A Production-Oriented Framework for Evaluation of SFX Generation

arXiv 2607.09973 ↗First posted 10 Jul 2026cs.SD cs.AI cs.SYcited by 0

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Abstract (arXiv)

Industrial sound design requires audio generation systems that not only produce realistic audio, but also preserve the perceptual identity of a reference, support controllable variation, and remain efficient for practical workflows. Existing evaluations are usually tied to text-to-audio (TTA), unconditional, or task-specific settings, limiting assessment for reference-guided sound effects (SFX) variation. To address this gap, we present a production-oriented evaluation framework for structured comparison of heterogeneous audio generation and editing methods. Our framework identifies nine production requirements and explicitly accounts for differences in model capabilities, enabling comparison under a common production objective. A two-stage protocol is introduced: (1) a reference-guided audio-to-audio (ATA) variation task, in which all methods are evaluated under the same ESC-50 SFX adaptation setup, and (2) capability-specific analyses of native operations such as SFX morphing, temporal and energy alignment, inpainting, and targeted editing. This framework combines objective metrics (including FAD, ImageBind-based reference alignment, and diversity across generated variants), together with a human study of perceptual identity preservation and transient diagnosis. Our study reveals complementary strengths and trade-offs across baselines for different production needs. Among the full-generation baselines evaluated under a shared ATA setting, AudioX provides the strongest overall trade-off between reference alignment and diversity while still supporting SFX morphing. Other baselines remain most suitable for specific editing operations. Our framework establishes a structured evaluation and decision protocol for reference-guided SFX variation and provides a practical basis for designing future unified industrial audio generation pipelines. Audio demos are on the accompanying web page.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 31 of its 37 references that are in the map. First posted on arXiv 10 Jul 2026.

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References · 31

  1. 2023ImageBind: One Embedding Space To Bind Them Allcited by 1,175
  2. 2020VGGSound: A Large-scale Audio-Visual Datasetcited by 648
  3. 2024Movie Gen: A Cast of Media Foundation Modelscited by 644
  4. 2023AudioLDM: Text-to-Audio Generation with Latent Diffusion Modelscited by 599
  5. 2023AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretrainingcited by 344
  6. 2022AudioGen: Textually Guided Audio Generationcited by 295
  7. 2024Stable Audio Opencited by 197
  8. 2023Diff-Foley: Synchronized Video-to-Audio Synthesis with Latent Diffusion Modelscited by 134
  9. 2024MMAudio: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesiscited by 134
  10. 2023UniAudio: An Audio Foundation Model Toward Universal Audio Generationcited by 127
  11. 2024Seeing and Hearing: Open-domain Visual-Audio Generation with Diffusion Latent Alignerscited by 111
  12. 2024Tango 2: Aligning Diffusion-based Text-to-Audio Generations through Direct Preference Optimizationcited by 97
19 more
  1. 2023AUDIT: Audio Editing by Following Instructions with Latent Diffusion Modelscited by 83
  2. 2024Video-Guided Foley Sound Generation with Multimodal Controlscited by 49
  3. 2025ThinkSound: Chain-of-Thought Reasoning in Multimodal Large Language Models for Audio Generation and Editingcited by 34
  4. 2025SAM Audio: Segment Anything in Audiocited by 30
  5. 2024Sketch2Sound: Controllable Audio Generation via Time-Varying Signals and Sonic Imitationscited by 24
  6. 2025UniForm: A Unified Multi-Task Diffusion Transformer for Audio-Video Generationcited by 22
  7. 2024T-FOLEY: A Controllable Waveform-Domain Diffusion Model for Temporal-Event-Guided Foley Sound Synthesiscited by 19
  8. 2024AudioTime: A Temporally-aligned Audio-text Benchmark Datasetcited by 19
  9. 2024Prompt-guided Precise Audio Editing with Diffusion Modelscited by 18
  10. 2023Loop Copilot: Conducting AI Ensembles for Music Generation and Iterative Editingcited by 15
  11. 2023EDMSound: Spectrogram Based Diffusion Models for Efficient and High-Quality Audio Synthesiscited by 15
  12. 2024AudioEditor: A Training-Free Diffusion-Based Audio Editing Frameworkcited by 13
  13. 2025A2SB: Audio-to-Audio Schrodinger Bridgescited by 11
  14. 2025AudioMorphix: Training-free audio editing with diffusion probabilistic modelscited by 7
  15. 2025TTA-Bench: A Comprehensive Benchmark for Evaluating Text-to-Audio Modelscited by 7
  16. 2023DDSP-SFX: Acoustically-guided sound effects generation with differentiable digital signal processingcited by 5
  17. 2021Onoma-to-wave: Environmental sound synthesis from onomatopoeic wordscited by 4
  18. 2026AC-Foley: Reference-Audio-Guided Video-to-Audio Synthesis with Acoustic Transfercited by 4
  19. 2024SoundMorpher: Perceptually-Uniform Sound Morphing with Diffusion Modelcited by 3

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