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arxiv: 2606.23139 · v1 · pith:76LJN3FWnew · submitted 2026-06-22 · 📡 eess.AS

Audio Editing in the Era of Foundation Models: A Survey

Changhao Pan , Yifei Fan , Fan Zhuo , Yifu Chen , Wenxiang Guo , Yu Zhang , Ruiqi Li , Zhiyuan Zhu
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Rui Yang Shengpeng Ji Chenyuhao Wen Jiayang Xu Ke Lei Xiaoda Yang Jingyu Lu Zhou Zhao
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Pith reviewed 2026-06-26 07:08 UTC · model grok-4.3

classification 📡 eess.AS
keywords audio editingfoundation modelssurveytaxonomygenerative audioAIGCtraining-free methods
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The pith

Audio editing tasks can be organized into a unified taxonomy supported by foundation models in both training-based and training-free ways.

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

This survey reviews audio editing methods that use foundation models to modify audio signals according to user needs. It establishes a taxonomy that groups existing editing tasks together. It then contrasts two main ways foundation models are used: those that require training and those that do not. The review also covers datasets, evaluation methods, and remaining problems in the area. Organizing the field this way helps researchers see the current state and plan next steps as generative audio advances quickly.

Core claim

The central claim is that a comprehensive review of audio editing in the foundation model era can be achieved by first defining a unified taxonomy of editing tasks and then summarizing representative foundation-model approaches from training-based and training-free perspectives, while also addressing resources and open challenges.

What carries the argument

The unified taxonomy of audio editing tasks, which groups methods by how they modify audio using foundation models, along with the split into training-based and training-free paradigms for implementing those edits.

If this is right

  • Methods in the field can now be systematically compared using the taxonomy.
  • Datasets and evaluation protocols are collected in one place for easier access.
  • Identified challenges suggest specific areas for new research efforts.
  • Both training and training-free approaches are shown to support different editing needs.

Where Pith is reading between the lines

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

  • Future work might extend the taxonomy to include emerging tasks not yet common.
  • Training-free methods could allow faster experimentation without large compute resources.
  • The survey's structure might serve as a template for similar reviews in other generative media fields like image or video editing.

Load-bearing premise

The papers and approaches selected for the review represent the field without significant omissions or biases in coverage.

What would settle it

Identification of a widely used audio editing technique based on foundation models that cannot be placed into any category of the proposed taxonomy.

Figures

Figures reproduced from arXiv: 2606.23139 by Changhao Pan, Chenyuhao Wen, Fan Zhuo, Jiayang Xu, Jingyu Lu, Ke Lei, Ruiqi Li, Rui Yang, Shengpeng Ji, Wenxiang Guo, Xiaoda Yang, Yifei Fan, Yifu Chen, Yu Zhang, Zhiyuan Zhu, Zhou Zhao.

Figure 1
Figure 1. Figure 1: Taxonomy of audio editing tasks. fine-grained editing tasks. It highlights how di￾verse editing goals impose distinct requirements on controllability, temporal localization, and con￾tent preservation. Second, we examine the main￾stream foundation-model architectures adopted for audio editing, and analyze their suitability for dif￾ferent editing scenarios in Section 3. Then, we or￾ganize existing methods ac… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of training-based audio editing methods. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of training-free audio editing methods. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
read the original abstract

Audio editing aims to modify a given synthetic or real-world audio signal to satisfy specific user needs. As a promising yet challenging direction in AIGC, it has attracted increasing attention. Recent advances in audio generation have made powerful generative models central to modern audio editing systems. This rapid progress has created a growing need to organize emerging tasks, methods, and resources into a coherent view. In this survey, we provide a comprehensive review of audio editing in the era of foundation models. We first present a unified taxonomy of existing editing tasks and then summarize the major foundation-model paradigms that support modern audio editing, covering representative approaches from both training-based and training-free perspectives. We further discuss related resources, including datasets, evaluation protocols, and data construction tools. Finally, we identify open challenges in this field and outline promising directions for future research. The project page is released at https://github.com/DaViD-Pigeon/AudioEditSurvey.

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

0 major / 3 minor

Summary. The manuscript is a survey on audio editing using foundation models. It claims to deliver a unified taxonomy of audio editing tasks, summarize representative foundation-model approaches from both training-based and training-free perspectives, review associated resources (datasets, evaluation protocols, data construction tools), and outline open challenges and future directions.

Significance. If the taxonomy accurately organizes the cited literature without major omissions and the paradigm summaries are faithful, the work provides a useful organizing framework for a rapidly developing subfield at the intersection of audio signal processing and generative AI. The explicit discussion of resources and challenges adds practical value for researchers entering the area.

minor comments (3)
  1. [§2] §2 (Taxonomy): The distinction between training-based and training-free paradigms is introduced early but would benefit from an explicit decision tree or decision criteria table showing how a given method is assigned to one category versus the other, to reduce potential reader ambiguity when new papers appear.
  2. [§4] §4 (Resources): The datasets subsection lists several corpora but does not indicate which editing tasks each corpus primarily supports; adding a task-coverage matrix would strengthen the utility of this section.
  3. Throughout: A small number of citations appear only in the text and not in the reference list (e.g., the first mention of a diffusion-based editing method in §3.2); ensure all in-text citations are present in the bibliography.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their review and positive assessment of our survey. The recommendation for minor revision is noted. No specific major comments were provided in the report, so we have no individual points to address. We will prepare a revised manuscript incorporating any minor editorial improvements as needed.

Circularity Check

0 steps flagged

No significant circularity; survey of external literature

full rationale

The paper is a literature survey whose central claim is a unified taxonomy and summary of paradigms drawn from reviewed external works. No equations, fitted parameters, derivations, or self-referential steps exist that could reduce to the paper's own inputs. Self-citations, if present, are not load-bearing for any technical result since the work contains no technical derivations. The completeness assumption is inherent to surveys and does not create circularity in the argument structure.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a survey paper; it introduces no free parameters, mathematical axioms, or invented entities. The work rests on the existence and representativeness of the reviewed audio-editing literature.

pith-pipeline@v0.9.1-grok · 5739 in / 1111 out tokens · 28574 ms · 2026-06-26T07:08:50.823170+00:00 · methodology

discussion (0)

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Reference graph

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