@inproceedings{pan-etal-2026-audio,
title = "Audio Editing in the Era of Foundation Models: A Survey",
author = "Pan, Changhao and
Fan, YiFei and
Zhuo, Fan and
Chen, Yifu and
Guo, Wenxiang and
Zhang, Yu and
Zhu, Zhiyuan and
Yang, Rui and
Ji, Shengpeng and
Wen, Chenyuhao and
Xu, Jiayang and
Lei, Ke and
Yang, Xiaoda and
Lu, Jingyu and
Zhao, Zhou",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {IJCNLP}-{AACL} 2026",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-aacl.78/",
pages = "1536--1559",
ISBN = "979-8-89176-444-6",
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 accompanying repository is released at https://github.com/MM-Speech/AudioEditSurvey."
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<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 accompanying repository is released at https://github.com/MM-Speech/AudioEditSurvey.</abstract>
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%0 Conference Proceedings
%T Audio Editing in the Era of Foundation Models: A Survey
%A Pan, Changhao
%A Fan, YiFei
%A Zhuo, Fan
%A Chen, Yifu
%A Guo, Wenxiang
%A Zhang, Yu
%A Zhu, Zhiyuan
%A Yang, Rui
%A Ji, Shengpeng
%A Wen, Chenyuhao
%A Xu, Jiayang
%A Lei, Ke
%A Yang, Xiaoda
%A Lu, Jingyu
%A Zhao, Zhou
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F pan-etal-2026-audio
%X 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 accompanying repository is released at https://github.com/MM-Speech/AudioEditSurvey.
%U https://aclanthology.org/2026.findings-aacl.78/
%P 1536-1559
Markdown (Informal)
[Audio Editing in the Era of Foundation Models: A Survey](https://aclanthology.org/2026.findings-aacl.78/) (Pan et al., Findings 2026)
ACL
- Changhao Pan, YiFei Fan, Fan Zhuo, Yifu Chen, Wenxiang Guo, Yu Zhang, Zhiyuan Zhu, Rui Yang, Shengpeng Ji, Chenyuhao Wen, Jiayang Xu, Ke Lei, Xiaoda Yang, Jingyu Lu, and Zhou Zhao. 2026. Audio Editing in the Era of Foundation Models: A Survey. In Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026, pages 1536–1559, Hengqin, China. Association for Computational Linguistics.