Audio Editing

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7 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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DirectAudioEdit: Inversion-Free Text-Guided Audio Editing via Diffusion Prediction Contrast
Jun 5, 2026Zhengkun Ge, Xiaoqian Liu, Haoran Zhang +5Audio Editing Audio Diffusion Models
School of Computer Science and Engineering, Northeastern University, Shenyang, China · Kunming University of Science and Technology · NiuTrans Research, Shenyang, China
MMAE: A Massive Multitask Audio Editing Benchmark
Jun 5, 2026Ziyang Ma, Ruiqi Yan, Ruiyang Xu +35Audio Editing
Shanghai Jiao Tong University · Shanghai Innovation Institute · Nanyang Technological University +5
SpeechEditBench: A Bilingual Multi-Attribute Benchmark for Instruction-Guided Speech Editing
Jun 1, 2026Hanlin Zhang, Daxin Tan, Dehua Tao +3Audio-Language Model Evaluation Audio Editing
Department of Computer Science, City University of Hong Kong · AI Lab, Leibniz Research Center, Huawei
UNISON: A Unified Sound Generation and Editing Framework via Deep LLM Fusion
May 29, 2026Zhaoqing Li, Haoning Xu, Jingran Su +9Text-to-Audio Generation TTS Synthesis
The Chinese University of Hong Kong · The Hong Kong Polytechnic University · City University of Hong Kong +3
AnchorSteer: Self-Discovered Concept Injection for Structure-Preserving Music Editing
May 29, 2026Chih-Heng Chang, Keng-Seng Ho, Chih-Yu Tsai +3Audio Editing Controllable Music Generation
National Taiwan University Taipei, Taiwan
Stable Audio 3
May 18, 2026Zach Evans, Julian D. Parker, Matthew Rice +4Text-to-Audio Generation Latent Diffusion Models
Stability AI
Exploring Token-Space Manipulation in Latent Audio Tokenizers
May 11, 2026Francesco Paissan, Luca Della Libera, Mirco Ravanelli +1Audio Representation Learning Neural Audio Codecs
1Mila – Québec AI Institute · 2Université Laval · 3Concordia University
Polyphonia: Zero-Shot Timbre Transfer in Polyphonic Music with Acoustic-Informed Attention Calibration
May 11, 2026Haowen Li, Tianxiang Li, Yi Yang +2Audio Editing Controllable Music Generation
School of Future Technology, South China University of Technology, Guangzhou, China
AST: Adaptive, Seamless, and Training-Free Precise Speech Editing
Apr 17, 2026Sihan Lv, Yechen Jin, Zhen Li +5TTS Synthesis Audio Editing
School of Software Technology, Zhejiang University, Hangzhou, China · Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing, China · Innovation and Management Center of the School of Software (Ningbo), Zhejiang University, Ningbo, China +1
SemanticAudio: Audio Generation and Editing in Semantic Space
Jan 29, 2026Zheqi Dai, Guangyan Zhang, Haolin He +5Text-to-Audio Generation Audio Editing
The Chinese University of Hong Kong · LIGHTSPEED · Shanghai Jiao Tong University +1
CosyEdit: Unlocking End-to-End Speech Editing Capability from Zero-Shot Text-to-Speech Models
Jan 8, 2026Junyang Chen, Yuhang Jia, Hui Wang +2Audio Editing Speech Editing
College of Computer Science, Nankai University, Tianjin, China

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DirectAudioEdit: Inversion-Free Text-Guided Audio Editing via Diffusion Prediction Contrast

Zhengkun Ge, Xiaoqian Liu, Haoran Zhang, Yuan Ge+4

School of Computer Science and Engineering, Northeastern University, Shenyang, China · Kunming University of Science and Technology · NiuTrans Research, Shenyang, China

Audio Editing Audio Diffusion Models Diffusion Guidance

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Text-guided audio editing aims to modify the language-specified acoustic content while preserving edit-irrelevant source components. Existing training-free methods typically rely on inversion-based editing. While inversion-free editing is appealing as it decreases computational overhead and reconstruction errors, it remains largely unexplored for audio editing. The key challenge is to construct a source-to-target editing path through diffusion denoising dynamics. In this paper, we introduce DirectAudioEdit, the first attempt to develop a training-free and inversion-free method for audio editing. Experiments on music and event-level benchmarks across two backbones show that DirectAudioEdit reduces macro-averaged FAD and KL by 15.9% and 15.8% compared with DDPM inversion, while achieving up to 64.5% editing speedup.

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