A melody perception music re-orchestration method and system based on structure-texture feature decoupling

By constructing a weakly paired dataset and training a contrastive melody encoder, acoustic texture information is extracted, and dual-flow control is combined to solve the problems of feature coupling and text control failure in melody conditional music generation, thus achieving high-fidelity melody adaptation across styles and instruments.

CN122073114BActive Publication Date: 2026-07-10SHANGHAI ARTIFICIAL INTELLIGENCE INNOVATION CENT
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SHANGHAI ARTIFICIAL INTELLIGENCE INNOVATION CENT
Filing Date
2026-04-22
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

In existing melody conditional music generation methods, the coupling of structural and texture features leads to model overfitting and text control failure, making it impossible to effectively achieve melody adaptation across styles and instruments.

Method used

By constructing a weakly paired dataset, a contrastive melody encoder and an adaptation generation model are trained. Contrastive learning and sparsification are used to strip away acoustic texture information, extract pure melody structure representations, and combine dual flow conditional control to generate music that conforms to the target audio information.

Benefits of technology

It enables cross-style and cross-instrument melody adaptation, improves text control compliance and model generalization ability, ensures melody consistency and audio quality, and adapts to music generation in complex scenarios.

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Abstract

The application discloses a melody-perceived music reorchestration method and system based on structure-texture feature decoupling. The method comprises the following steps: constructing a weakly paired dataset comprising audio pairs with the same melody skeleton and different acoustic textures, training a comparative melody encoder based on the dataset to extract a pure melody structure representation, training a dual-flow flux reorchestration generation model, and finally completing the reorchestration generation of target audio. The system comprises a dataset construction module, an encoder training module, a generation model training module and an inference generation module, and the core comprises a comparative melody encoder and a dual-flow flux reorchestration generation model. The application solves the problems of feature entanglement and text control failure, improves melody consistency and model generalization ability, and can be widely applied to the fields of digital music creation, short video content production, old audio reproduction and the like.
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Citation Information

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