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arxiv: 2310.12858 · v3 · pith:H2JTFPAOnew · submitted 2023-10-19 · 💻 cs.SD · cs.LG· eess.AS

Audio Editing with Non-Rigid Text Prompts

Francesco Paissan , Luca Della Libera , Zhepei Wang , Mirco Ravanelli , Paris Smaragdis , Cem Subakan This is my paper
classification 💻 cs.SD cs.LGeess.AS
keywords audioeditstextableeditingexplorefaithfulinput
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In this paper, we explore audio-editing with non-rigid text edits. We show that the proposed editing pipeline is able to create audio edits that remain faithful to the input audio. We explore text prompts that perform addition, style transfer, and in-painting. We quantitatively and qualitatively show that the edits are able to obtain results which outperform Audio-LDM, a recently released text-prompted audio generation model. Qualitative inspection of the results points out that the edits given by our approach remain more faithful to the input audio in terms of keeping the original onsets and offsets of the audio events.

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Cited by 2 Pith papers

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

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    AnchorSteer couples self-discovered semantic concept vectors with structural anchoring in diffusion models to achieve controllable music editing with preserved structure.

  2. Audio Editing in the Era of Foundation Models: A Survey

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    A survey that presents a unified taxonomy of audio editing tasks, summarizes training-based and training-free foundation model approaches, reviews datasets and evaluation protocols, and identifies future challenges.