# A Production-Oriented Framework for Evaluation of SFX Generation

arXiv: https://arxiv.org/abs/2607.09973 · HTML: https://mlsanity.com/paper/2607.09973/

## 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.

## Builds on (most cited first, 30 of 31)

- [ImageBind: One Embedding Space To Bind Them All](https://mlsanity.com/paper/2305.05665/) (2023)
- [VGGSound: A Large-scale Audio-Visual Dataset](https://mlsanity.com/paper/2004.14368/) (2020)
- [Movie Gen: A Cast of Media Foundation Models](https://mlsanity.com/paper/2410.13720/) (2024)
- [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://mlsanity.com/paper/2301.12503/) (2023)
- [AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining](https://mlsanity.com/paper/2308.05734/) (2023)
- [AudioGen: Textually Guided Audio Generation](https://mlsanity.com/paper/2209.15352/) (2022)
- [Stable Audio Open](https://mlsanity.com/paper/2407.14358/) (2024)
- [Diff-Foley: Synchronized Video-to-Audio Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2306.17203/) (2023)
- [MMAudio: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis](https://mlsanity.com/paper/2412.15322/) (2024)
- [UniAudio: An Audio Foundation Model Toward Universal Audio Generation](https://mlsanity.com/paper/2310.00704/) (2023)
- [Seeing and Hearing: Open-domain Visual-Audio Generation with Diffusion Latent Aligners](https://mlsanity.com/paper/2402.17723/) (2024)
- [Tango 2: Aligning Diffusion-based Text-to-Audio Generations through Direct Preference Optimization](https://mlsanity.com/paper/2404.09956/) (2024)
- [AUDIT: Audio Editing by Following Instructions with Latent Diffusion Models](https://mlsanity.com/paper/2304.00830/) (2023)
- [Video-Guided Foley Sound Generation with Multimodal Controls](https://mlsanity.com/paper/2411.17698/) (2024)
- [ThinkSound: Chain-of-Thought Reasoning in Multimodal Large Language Models for Audio Generation and Editing](https://mlsanity.com/paper/2506.21448/) (2025)
- [SAM Audio: Segment Anything in Audio](https://mlsanity.com/paper/2512.18099/) (2025)
- [Sketch2Sound: Controllable Audio Generation via Time-Varying Signals and Sonic Imitations](https://mlsanity.com/paper/2412.08550/) (2024)
- [UniForm: A Unified Multi-Task Diffusion Transformer for Audio-Video Generation](https://mlsanity.com/paper/2502.03897/) (2025)
- [T-FOLEY: A Controllable Waveform-Domain Diffusion Model for Temporal-Event-Guided Foley Sound Synthesis](https://mlsanity.com/paper/2401.09294/) (2024)
- [AudioTime: A Temporally-aligned Audio-text Benchmark Dataset](https://mlsanity.com/paper/2407.02857/) (2024)
- [Prompt-guided Precise Audio Editing with Diffusion Models](https://mlsanity.com/paper/2406.04350/) (2024)
- [Loop Copilot: Conducting AI Ensembles for Music Generation and Iterative Editing](https://mlsanity.com/paper/2310.12404/) (2023)
- [EDMSound: Spectrogram Based Diffusion Models for Efficient and High-Quality Audio Synthesis](https://mlsanity.com/paper/2311.08667/) (2023)
- [AudioEditor: A Training-Free Diffusion-Based Audio Editing Framework](https://mlsanity.com/paper/2409.12466/) (2024)
- [A2SB: Audio-to-Audio Schrodinger Bridges](https://mlsanity.com/paper/2501.11311/) (2025)
- [AudioMorphix: Training-free audio editing with diffusion probabilistic models](https://mlsanity.com/paper/2505.16076/) (2025)
- [TTA-Bench: A Comprehensive Benchmark for Evaluating Text-to-Audio Models](https://mlsanity.com/paper/2509.02398/) (2025)
- [DDSP-SFX: Acoustically-guided sound effects generation with differentiable digital signal processing](https://mlsanity.com/paper/2309.08060/) (2023)
- [Onoma-to-wave: Environmental sound synthesis from onomatopoeic words](https://mlsanity.com/paper/2102.05872/) (2021)
- [AC-Foley: Reference-Audio-Guided Video-to-Audio Synthesis with Acoustic Transfer](https://mlsanity.com/paper/2603.15597/) (2026)

## 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.