S2Accompanist is a 402M-parameter semantic-aware diffusion model that achieves SOTA on the ATTM Grand Challenge benchmark for music accompaniment generation via automated data processing and structure-guided VAE fine-tuning.
arXiv preprint arXiv:2602.00744(2026)
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SongBench is a new fine-grained benchmark for song quality assessment with seven dimensions and an expert-annotated dataset of 11,717 samples showing high correlation with professional ratings.
LaDA-Band applies discrete masked diffusion with dual-track conditioning and progressive training to generate vocal-to-accompaniment tracks that improve acoustic authenticity, global coherence, and dynamic orchestration over prior baselines.
SAME is a semantically regularized transformer autoencoder for music that delivers 4096x compression with open-weights release of large and small variants.
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S2Accompanist: A Semantic-Aware and Structure-Guided Diffusion Model for Music Accompaniment Generation
S2Accompanist is a 402M-parameter semantic-aware diffusion model that achieves SOTA on the ATTM Grand Challenge benchmark for music accompaniment generation via automated data processing and structure-guided VAE fine-tuning.
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SongBench: A Fine-Grained Multi-Aspect Benchmark for Song Quality Assessment
SongBench is a new fine-grained benchmark for song quality assessment with seven dimensions and an expert-annotated dataset of 11,717 samples showing high correlation with professional ratings.
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LaDA-Band: Language Diffusion Models for Vocal-to-Accompaniment Generation
LaDA-Band applies discrete masked diffusion with dual-track conditioning and progressive training to generate vocal-to-accompaniment tracks that improve acoustic authenticity, global coherence, and dynamic orchestration over prior baselines.
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SAME: A Semantically-Aligned Music Autoencoder
SAME is a semantically regularized transformer autoencoder for music that delivers 4096x compression with open-weights release of large and small variants.