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NotaGen: Advancing Musicality in Symbolic Music Generation with Large Language Model Training Paradigms
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We introduce NotaGen, a symbolic music generation model aiming to explore the potential of producing high-quality classical sheet music. Inspired by the success of Large Language Models (LLMs), NotaGen adopts pre-training, fine-tuning, and reinforcement learning paradigms (henceforth referred to as the LLM training paradigms). It is pre-trained on 1.6M pieces of music in ABC notation, and then fine-tuned on approximately 9K high-quality classical compositions conditioned on "period-composer-instrumentation" prompts. For reinforcement learning, we propose the CLaMP-DPO method, which further enhances generation quality and controllability without requiring human annotations or predefined rewards. Our experiments demonstrate the efficacy of CLaMP-DPO in symbolic music generation models with different architectures and encoding schemes. Furthermore, subjective A/B tests show that NotaGen outperforms baseline models against human compositions, greatly advancing musical aesthetics in symbolic music generation.
Forward citations
Cited by 7 Pith papers
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Text2Score: Generating Sheet Music From Textual Prompts
Text2Score turns text prompts into sheet music by having an LLM produce a bar-wise structural plan and a hierarchical decoder write ABC notation from that plan.
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Amadeus generates symbolic music by autoregressively predicting note-level latents and decoding their attributes in parallel with a masked discrete diffusion model, yielding faster and more controllable generation tha...
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Large Language Models' Internal Perception of Symbolic Music
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WeaveMuse: An Open Agentic System for Multimodal Music Understanding and Generation
An open multi-agent system that orchestrates specialized music models for understanding, composition, and synthesis, with local or hosted deployment.
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CoComposer: LLM Multi-agent Collaborative Music Composition
A five-agent LLM system for ABC-notation composition scores modestly higher than ComposerX and a single LLM on an automated aesthetic model, but no error bars or significance tests are reported.
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