pith. sign in

arxiv: 2509.14003 · v2 · submitted 2025-09-17 · 💻 cs.SD · cs.AI

RFM-Editing: Rectified Flow Matching for Text-guided Audio Editing

Liting Gao , Yi Yuan , Yaru Chen , Yuelan Cheng , Zhenbo Li , Juan Wen , Shubin Zhang , Wenwu Wang This is my paper

Pith reviewed 2026-05-18 16:30 UTC · model grok-4.3

classification 💻 cs.SD cs.AI
keywords text-guided audio editingrectified flow matchingdiffusion modelsaudio generationmulti-event audiosemantic alignmentsound editing
0
0 comments X

The pith

Rectified flow matching lets text prompts edit specific parts of overlapping audio without masks or captions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces an end-to-end framework called RFM-Editing that uses rectified flow matching to modify targeted content in an audio signal according to a text prompt. It builds a dataset of overlapping multi-event sounds to train and evaluate the model in complex scenarios. The approach focuses on precise localization and faithful semantic changes while leaving unrelated parts of the audio untouched. Experiments indicate this works without needing auxiliary captions, masks, or costly optimization steps. If the results hold, audio editing becomes simpler for real-world cases with multiple simultaneous sounds.

Core claim

We propose RFM-Editing, a novel rectified flow matching-based diffusion framework for text-guided audio editing. By training on a constructed dataset of overlapping multi-event audio, the model achieves faithful semantic alignment according to the text prompt without requiring auxiliary captions or masks, while maintaining competitive editing quality across metrics.

What carries the argument

Rectified flow matching diffusion model that transforms noise to audio while localizing and altering only the prompt-specified content.

If this is right

  • Editing succeeds on audio containing multiple overlapping events using only a text prompt.
  • No auxiliary captions or masks are needed during training or inference.
  • Editing quality stays competitive with existing methods on standard metrics.
  • The end-to-end framework avoids separate optimization or zero-shot procedures.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same flow-matching approach could be tested on longer or streaming audio for practical editing tools.
  • Prompt-only control might extend to related tasks such as speech modification or environmental sound design.
  • Comparing performance on real-world noisy recordings would test whether the dataset fully captures generalization needs.

Load-bearing premise

The constructed dataset of overlapping multi-event audio is representative of real-world editing scenarios so the model generalizes beyond the training distribution.

What would settle it

Run the model on a held-out collection of natural, unscripted recordings with overlapping events absent from the training dataset and check whether semantic alignment and editing quality remain competitive.

read the original abstract

Diffusion models have shown remarkable progress in text-to-audio generation. However, text-guided audio editing remains in its early stages. This task focuses on modifying the target content within an audio signal while preserving the rest, thus demanding precise localization and faithful editing according to the text prompt. Existing training-based and zero-shot methods that rely on full-caption or costly optimization often struggle with complex editing or lack practicality. In this work, we propose a novel end-to-end efficient rectified flow matching-based diffusion framework for audio editing, and construct a dataset featuring overlapping multi-event audio to support training and benchmarking in complex scenarios. Experiments show that our model achieves faithful semantic alignment without requiring auxiliary captions or masks, while maintaining competitive editing quality across metrics.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes RFM-Editing, a rectified flow matching-based diffusion framework for text-guided audio editing. The authors construct a dataset of overlapping multi-event audio to support training and benchmarking in complex scenarios. They claim the model achieves faithful semantic alignment without auxiliary captions or masks while maintaining competitive editing quality across metrics.

Significance. If substantiated, this would advance practical text-guided audio editing by enabling end-to-end editing of complex multi-event audio without masks or full captions. Rectified flow matching could improve efficiency and stability relative to standard diffusion models for this task.

major comments (2)
  1. [Dataset Construction] Dataset Construction: The paper constructs a dataset featuring overlapping multi-event audio to support complex scenarios and generalization claims, yet supplies no quantitative characterization of event overlap statistics, source diversity, acoustic variability, or direct comparison to external real recordings (e.g., AudioSet or FSD50K subsets). This is load-bearing for the central claim that training on the constructed dataset enables faithful semantic alignment and competitive quality while generalizing beyond the training distribution.
  2. [Experiments] Experiments: The abstract and main text report competitive metrics and faithful semantic alignment, but the manuscript provides no quantitative tables, baselines, ablation studies, or error analysis. This prevents verification of the soundness of the reported results.
minor comments (1)
  1. [Abstract] The abstract could specify the exact metrics used to claim 'competitive editing quality' and note key dataset statistics.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments. We address each major comment below and indicate where revisions will be made to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Dataset Construction] Dataset Construction: The paper constructs a dataset featuring overlapping multi-event audio to support complex scenarios and generalization claims, yet supplies no quantitative characterization of event overlap statistics, source diversity, acoustic variability, or direct comparison to external real recordings (e.g., AudioSet or FSD50K subsets). This is load-bearing for the central claim that training on the constructed dataset enables faithful semantic alignment and competitive quality while generalizing beyond the training distribution.

    Authors: We agree that additional quantitative details would better support the claims. In the revised manuscript we will add a table and accompanying text reporting event overlap statistics (e.g., fraction of clips with 2+ overlapping events, mean overlap duration), source diversity (unique event classes and their frequencies), acoustic variability (duration, SNR, and spectral statistics), and side-by-side comparisons with matched subsets of AudioSet and FSD50K on the same metrics. revision: yes

  2. Referee: [Experiments] Experiments: The abstract and main text report competitive metrics and faithful semantic alignment, but the manuscript provides no quantitative tables, baselines, ablation studies, or error analysis. This prevents verification of the soundness of the reported results.

    Authors: We acknowledge that the quantitative results section would benefit from clearer presentation. The revised manuscript will include explicit tables with numerical metrics (CLAP, FID, perceptual scores), direct comparisons to published baselines, ablation studies isolating the rectified-flow component, and a dedicated error-analysis subsection discussing failure modes and limitations. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation grounded in external flow-matching literature

full rationale

The paper introduces an end-to-end rectified flow matching framework for text-guided audio editing and constructs a synthetic overlapping multi-event dataset for training. No equations, fitted parameters, or predictions are shown that reduce by construction to self-defined quantities or prior self-citations. The method is presented as building on standard flow-matching techniques from the broader literature, with performance claims resting on experimental metrics rather than tautological re-derivations. Dataset construction is an explicit modeling choice and input assumption, not a circular step. This is the common honest case of a self-contained empirical method without load-bearing self-referential loops.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that rectified flow matching can be conditioned on text to perform localized edits while preserving non-target content; no free parameters or new entities are introduced in the abstract.

axioms (1)
  • domain assumption Rectified flow matching trajectories can be conditioned on text prompts to achieve localized semantic edits in audio without explicit masks.
    Invoked to justify the end-to-end framework for complex overlapping audio.

pith-pipeline@v0.9.0 · 5665 in / 1133 out tokens · 39276 ms · 2026-05-18T16:30:10.503395+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

32 extracted references · 32 canonical work pages · 4 internal anchors

  1. [1]

    RFM-Editing: Rectified Flow Matching for Text-guided Audio Editing

    INTRODUCTION Recent advancements in diffusion-based generative modeling have led to remarkable progress in high-quality text-to-audio (TTA) generation, with examples including denoising diffu- sion probabilistic model (DDPM)-based methods [1] (Audi- oLDM [2, 3], Make-An-Audio [4, 5]) and flow-based meth- ods [6] (TangoFlux [7]). Text-guided audio editing ...

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  2. [2]

    1 shows the training and inference-time editing pipeline of RFM-Editing, the first unified RFM-based instruction- guided audio editing model that jointly trains three editing tasks

    PROPOSED METHOD Fig. 1 shows the training and inference-time editing pipeline of RFM-Editing, the first unified RFM-based instruction- guided audio editing model that jointly trains three editing tasks. Built upon the LDM [17], RFM-Editing integrates an audio feature extractor, a low-rank adaptation (LoRA [20])- tuned text encoder for instruction understa...

    work page

  3. [3]

    A HiFi- GAN vocoder [21] is then used to convert the spectrogram into a waveform, producing the final edited audio output

    Finally,x ∗ 0 is decoded by the V AE decoder to recon- struct the log-mel spectrogram of the edited audio. A HiFi- GAN vocoder [21] is then used to convert the spectrogram into a waveform, producing the final edited audio output

    work page

  4. [4]

    beeps” and “barking

    EXPERIMENTS 3.1. Datasets We construct an instruction-based audio editing dataset using AudioCaps2 [19]. The DeepSeek API is used to count sound events in each caption. Audio clips with more than three events are excluded, as they tend to be noisy and less suitable for training, and those containing only one event as single- event clips for composition. W...

    work page

  5. [5]

    Ex- periments show that RFM-Editing can automatically localize instruction-relevant time frames, achieving faithful alignment with target semantics and precise editing

    CONCLUSION We have presented RFM-Editing, the first rectified flow matching framework for instruction-guided audio editing without captions or masks, along with a new dataset. Ex- periments show that RFM-Editing can automatically localize instruction-relevant time frames, achieving faithful alignment with target semantics and precise editing. Results high...

    work page

  6. [6]

    Denoising diffusion probabilis- tic models,

    J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilis- tic models,”Advances in neural information processing sys- tems, vol. 33, pp. 6840–6851, 2020

    work page 2020

  7. [7]

    Audioldm: text-to-audio generation with latent diffusion models,

    H. Liu, Z. Chen, Y . Yuan, X. Mei, X. Liu, D. Mandic, W. Wang, and M. D. Plumbley, “Audioldm: text-to-audio generation with latent diffusion models,” inProceedings of the 40th Interna- tional Conference on Machine Learning, 2023, pp. 21 450– 21 474

    work page 2023

  8. [8]

    Audioldm 2: Learn- ing holistic audio generation with self-supervised pretraining,

    H. Liu, Y . Yuan, X. Liu, X. Mei, Q. Kong, Q. Tian, Y . Wang, W. Wang, Y . Wang, and M. D. Plumbley, “Audioldm 2: Learn- ing holistic audio generation with self-supervised pretraining,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 32, pp. 2871–2883, 2024

    work page 2024

  9. [9]

    Make-an-audio: Text-to-audio generation with prompt-enhanced diffusion models,

    R. Huang, J. Huang, D. Yang, Y . Ren, L. Liu, M. Li, Z. Ye, J. Liu, X. Yin, and Z. Zhao, “Make-an-audio: Text-to-audio generation with prompt-enhanced diffusion models,” inInter- national Conference on Machine Learning. PMLR, 2023, pp. 13 916–13 932

    work page 2023

  10. [10]

    Make-An-Audio 2: Temporal-enhanced text-to- audio generation,

    J. Huang, Y . Ren, R. Huang, D. Yang, Z. Ye, C. Zhang, J. Liu, X. Yin, Z. Ma, and Z. Zhao, “Make-an-audio 2: Temporal-enhanced text-to-audio generation,”arXiv preprint arXiv:2305.18474, 2023

    work page arXiv 2023

  11. [11]

    Flow Matching for Generative Modeling

    Y . Lipman, R. T. Chen, H. Ben-Hamu, M. Nickel, and M. Le, “Flow matching for generative modeling,”arXiv preprint arXiv:2210.02747, 2022

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  12. [12]

    Tan- goflux: Super fast and faithful text to audio generation with flow matching and clap-ranked preference optimization,

    C.-Y . Hung, N. Majumder, Z. Kong, A. Mehrish, A. A. Bagherzadeh, C. Li, R. Valle, B. Catanzaro, and S. Poria, “Tan- goflux: Super fast and faithful text to audio generation with flow matching and clap-ranked preference optimization,”arXiv preprint arXiv:2412.21037, 2024

    work page arXiv 2024

  13. [13]

    Audioeditor: A training-free diffusion-based audio editing framework,

    Y . Jia, Y . Chen, J. Zhao, S. Zhao, W. Zeng, Y . Chen, and Y . Qin, “Audioeditor: A training-free diffusion-based audio editing framework,” inICASSP 2025-2025 IEEE International Con- ference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025, pp. 1–5

    work page 2025

  14. [14]

    Prompt- guided precise audio editing with diffusion models,

    M. Xu, C. Li, D. Zhang, D. Su, W. Liang, and D. Yu, “Prompt- guided precise audio editing with diffusion models,” inPro- ceedings of the 41st International Conference on Machine Learning, 2024, pp. 55 126–55 143

    work page 2024

  15. [15]

    Zero-shot unsupervised and text-based audio editing using ddpm inversion.arXiv preprint arXiv:2402.10009,

    H. Manor and T. Michaeli, “Zero-shot unsupervised and text- based audio editing using ddpm inversion,”arXiv preprint arXiv:2402.10009, 2024

    work page arXiv 2024

  16. [16]

    Audit: Audio editing by following instructions with latent diffusion models,

    Y . Wang, Z. Ju, X. Tan, L. He, Z. Wu, J. Bianet al., “Audit: Audio editing by following instructions with latent diffusion models,”Advances in Neural Information Processing Systems, vol. 36, pp. 71 340–71 357, 2023

    work page 2023

  17. [17]

    Audio editing with non-rigid text prompts,

    F. Paissan, L. Della Libera, Z. Wang, M. Ravanelli, P. Smaragdis, C. Subakanet al., “Audio editing with non-rigid text prompts,” inProceedings of INTERSPEECH 2024, 2024

    work page 2024

  18. [18]

    Auffusion: Leveraging the power of diffusion and large language models for text-to-audio generation,

    J. Xue, Y . Deng, Y . Gao, and Y . Li, “Auffusion: Leveraging the power of diffusion and large language models for text-to-audio generation,”IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2024

    work page 2024

  19. [19]

    Text- to-audio generation using instruction guided latent diffusion model,

    D. Ghosal, N. Majumder, A. Mehrish, and S. Poria, “Text- to-audio generation using instruction guided latent diffusion model,” inProceedings of the 31st ACM International Con- ference on Multimedia, 2023, pp. 3590–3598

    work page 2023

  20. [20]

    Prompt-to-prompt image editing with cross- attention control,

    A. Hertz, R. Mokady, J. Tenenbaum, K. Aberman, Y . Pritch, and D. Cohen-or, “Prompt-to-prompt image editing with cross- attention control,” inThe Eleventh International Conference on Learning Representations

    work page

  21. [21]

    Wavcraft: Audio edit- ing and generation with natural language prompts

    J. Liang, H. Zhang, H. Liu, Y . Cao, Q. Kong, X. Liu, W. Wang, M. Plumbley, H. Phan, and E. Benetos, “Wavcraft: Audio edit- ing and generation with natural language prompts.” ICLR 2024 Workshop on LLM Agents, 2024

    work page 2024

  22. [22]

    High-resolution image synthesis with latent diffusion models,

    R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Om- mer, “High-resolution image synthesis with latent diffusion models,” inProceedings of the IEEE/CVF conference on com- puter vision and pattern recognition, 2022, pp. 10 684–10 695

    work page 2022

  23. [23]

    Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

    X. Liu, C. Gong, and Q. Liu, “Flow straight and fast: Learning to generate and transfer data with rectified flow,”arXiv preprint arXiv:2209.03003, 2022

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  24. [24]

    AudioCaps: Gener- ating Captions for Audios in The Wild,

    C. D. Kim, B. Kim, H. Lee, and G. Kim, “AudioCaps: Gener- ating Captions for Audios in The Wild,” inNAACL-HLT, 2019

    work page 2019

  25. [25]

    Lora: Low-rank adaptation of large language models

    E. J. Hu, Y . Shen, P. Wallis, Z. Allen-Zhu, Y . Li, S. Wang, L. Wang, W. Chenet al., “Lora: Low-rank adaptation of large language models.”ICLR, vol. 1, no. 2, p. 3, 2022

    work page 2022

  26. [26]

    Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis,

    J. Kong, J. Kim, and J. Bae, “Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis,”Ad- vances in neural information processing systems, vol. 33, pp. 17 022–17 033, 2020

    work page 2020

  27. [27]

    Auto-Encoding Variational Bayes

    D. P. Kingma and M. Welling, “Auto-encoding variational bayes,”arXiv preprint arXiv:1312.6114, 2013

    work page internal anchor Pith review Pith/arXiv arXiv 2013

  28. [28]

    Scaling instruction-finetuned language models,

    H. W. Chung, L. Hou, S. Longpre, B. Zoph, Y . Tay, W. Fe- dus, Y . Li, X. Wang, M. Dehghani, S. Brahmaet al., “Scaling instruction-finetuned language models,”Journal of Machine Learning Research, vol. 25, no. 70, pp. 1–53, 2024

    work page 2024

  29. [29]

    Clap learning audio concepts from natural language supervision,

    B. Elizalde, S. Deshmukh, M. Al Ismail, and H. Wang, “Clap learning audio concepts from natural language supervision,” in ICASSP 2023-2023 IEEE International Conference on Acous- tics, Speech and Signal Processing (ICASSP). IEEE, 2023, pp. 1–5

    work page 2023

  30. [30]

    Dpm- solver: A fast ode solver for diffusion probabilistic model sam- pling in around 10 steps,

    C. Lu, Y . Zhou, F. Bao, J. Chen, C. Li, and J. Zhu, “Dpm- solver: A fast ode solver for diffusion probabilistic model sam- pling in around 10 steps,”Advances in neural information pro- cessing systems, vol. 35, pp. 5775–5787, 2022

    work page 2022

  31. [31]

    Panns: Large-scale pretrained audio neural net- works for audio pattern recognition,

    Q. Kong, Y . Cao, T. Iqbal, Y . Wang, W. Wang, and M. D. Plumbley, “Panns: Large-scale pretrained audio neural net- works for audio pattern recognition,”IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 28, pp. 2880–2894, 2020

    work page 2020

  32. [32]

    Cnn architectures for large-scale audio clas- sification,

    S. Hershey, S. Chaudhuri, D. P. Ellis, J. F. Gemmeke, A. Jansen, R. C. Moore, M. Plakal, D. Platt, R. A. Saurous, B. Seyboldet al., “Cnn architectures for large-scale audio clas- sification,” in2017 ieee international conference on acoustics, speech and signal processing (icassp). IEEE, 2017, pp. 131– 135

    work page 2017