DiffuseRoll: Multi-track multi-category music generation based on diffusion model
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Recent advancements in generative models have shown remarkable progress in music generation. However, most existing methods focus on generating monophonic or homophonic music, while the generation of polyphonic and multi-track music with rich attributes is still a challenging task. In this paper, we propose a novel approach for multi-track, multi-attribute symphonic music generation using the diffusion model. Specifically, we generate piano-roll representations with a diffusion model and map them to MIDI format for output. To capture rich attribute information, we introduce a color coding scheme to encode note sequences into color and position information that represents pitch,velocity, and instrument. This scheme enables a seamless mapping between discrete music sequences and continuous images. We also propose a post-processing method to optimize the generated scores for better performance. Experimental results show that our method outperforms state-of-the-art methods in terms of polyphonic music generation with rich attribute information compared to the figure methods.
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