Hybrid Diffusion Transformer for Instruction-Guided Audio Editing via Rectified Flow

Audio editing aims to modify specific content in an existing audio clip according to a natural language instruction while preserving the remaining acoustic content. Despite the remarkable progress of diffusion models, existing training-based editing methods mainly rely on the local inductive biases and cross-attention interaction in convolutional U-Net backbones, which often hinder long-range semantic alignment and precise understanding and localization of instructions. In contrast, diffusion tr

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ethics.ai (18 June 2026), “Hybrid Diffusion Transformer for Instruction-Guided Audio Editing via Rectified Flow,” evidence record 804, https://ethics.ai/record/804 (originally published by arXiv).

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