SongBloom: Coherent Song Generation via Interleaved Autoregressive Sketching and Diffusion Refinement

Generating music with coherent structure, harmonious instrumental and vocal elements remains a significant challenge in song generation. Existing language models and diffusion-based methods often struggle to balance global coherence with local fidelity, resulting in outputs that lack musicality or suffer from incoherent progression and mismatched lyrics. This paper introduces SongBloom, a novel framework for full-length song generation that leverages an interleaved paradigm of autoregressive sketching and diffusion-based refinement. SongBloom employs an autoregressive diffusion model that combines the high fidelity of diffusion models with the scalability of language models. Specifically, it gradually extends a musical sketch from short to long and refines the details from coarse to fine-grained. The interleaved generation paradigm effectively integrates prior semantic and acoustic context to guide the generation process. Experimental results demonstrate that SongBloom outperforms existing methods across both subjective and objective metrics and achieves performance comparable to the state-of-the-art commercial music generation platforms. Audio samples are available on our demo page: https: //cypress-yang. github. io/SongBloom_demo.

Authors

• Chenyu Yang The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), Shenzhen, China
• Shuai Wang Shenzhen Research Institute of Big Data
• Hangting Chen Tencent AI Lab
• Wei Tan Tencent AI Lab
• Jianwei Yu Tencent AI Lab
• Haizhou Li The Chinese University of Hong Kong, Shenzhen National University of Singapore Shenzhen Research Institute of Big Data

Keywords

No keywords are indexed for this paper.