Tutti: Expressive Multi-Singer Synthesis via Structure-Level Timbre Control and Vocal Texture Modeling
This work provides a novel paradigm for complex multi-singer arrangement in singing voice synthesis, benefiting applications in music production and entertainment.
The paper tackles the problem of synthesizing high-fidelity multi-singer performances by addressing limitations in dynamic singer arrangement and vocal texture, achieving significant enhancements in acoustic realism for choral generation.
While existing Singing Voice Synthesis systems achieve high-fidelity solo performances, they are constrained by global timbre control, failing to address dynamic multi-singer arrangement and vocal texture within a single song. To address this, we propose Tutti, a unified framework designed for structured multi-singer generation. Specifically, we introduce a Structure-Aware Singer Prompt to enable flexible singer scheduling evolving with musical structure, and propose Complementary Texture Learning via Condition-Guided VAE to capture implicit acoustic textures (e.g., spatial reverberation and spectral fusion) that are complementary to explicit controls. Experiments demonstrate that Tutti excels in precise multi-singer scheduling and significantly enhances the acoustic realism of choral generation, offering a novel paradigm for complex multi-singer arrangement. Audio samples are available at https://annoauth123-ctrl.github.io/Tutii_Demo/.