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Music editing primarily entails the modification of instrument tracks orremixing in the whole, which offers a novel reinterpretation of the originalpiece through a series of operations. These music processing methods holdimmense potential across various applications but demand substantial expertise.Prior methodologies, although effective for image and audio modifications,falter when directly applied to music. This is attributed to music'sdistinctive data nature, where such methods can inadvertently compromise theintrinsic harmony and coherence of music. In this paper, we develop InstructME,an Instruction guided Music Editing and remixing framework based on latentdiffusion models. Our framework fortifies the U-Net with multi-scaleaggregation in order to maintain consistency before and after editing. Inaddition, we introduce chord progression matrix as condition information andincorporate it in the semantic space to improve melodic harmony while editing.For accommodating extended musical pieces, InstructME employs a chunktransformer, enabling it to discern long-term temporal dependencies withinmusic sequences. We tested InstructME in instrument-editing, remixing, andmulti-round editing. Both subjective and objective evaluations indicate thatour proposed method significantly surpasses preceding systems in music quality,text relevance and harmony. Demo samples are available athttps://musicedit.github.io/

InstructME: An Instruction Guided Music Edit And Remix Framework with Latent Diffusion Models