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Zero-Shot Unsupervised and Text-Based Audio Editing Using DDPM Inversion

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arxiv 2402.10009 v4 pith:FCQ3EGNQ submitted 2024-02-15 cs.SD cs.LGeess.AS

Hila Manor , Tomer Michaeli This is my paper
classification cs.SDcs.LGeess.AS
keywords editingzero-shotaudiodomainsignalsddpmimageinversion
verification ladder T0 review T1 audit T2 compute T3 formal
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Editing signals using large pre-trained models, in a zero-shot manner, has recently seen rapid advancements in the image domain. However, this wave has yet to reach the audio domain. In this paper, we explore two zero-shot editing techniques for audio signals, which use DDPM inversion with pre-trained diffusion models. The first, which we coin ZEro-shot Text-based Audio (ZETA) editing, is adopted from the image domain. The second, named ZEro-shot UnSupervized (ZEUS) editing, is a novel approach for discovering semantically meaningful editing directions without supervision. When applied to music signals, this method exposes a range of musically interesting modifications, from controlling the participation of specific instruments to improvisations on the melody. Samples and code can be found in https://hilamanor.github.io/AudioEditing/ .

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Audio-Guided Visual Editing with Complex Multi-Modal Prompts

    cs.CV 2025-08 conditional novelty 6.0 of 10

    A training-free framework that maps audio embeddings into Stable Diffusion's text space and fuses multiple audio/text prompts via per-patch residual noise selection, outperforming text-only editors on new audio-visual...

  2. EditGen: Harnessing Cross-Attention Control for Instruction-Based Auto-Regressive Audio Editing

    cs.SD 2025-07 conditional novelty 6.0 of 10

    Prompt-to-Prompt cross-attention control is adapted to autoregressive audio generation, enabling training-free music editing that outperforms a diffusion baseline.

  3. FlowSonic: Stable Zero-Shot Music Editing via High-Order Trajectory Integration

    cs.SD 2026-07 reject novelty 4.0 of 10

    FlowSonic combines deterministic rectified-flow inversion, cached cross-attention injection, and a 'seeded' third-order Adams-Bashforth solver to report better timbre and genre edits on small datasets.

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