Diffusion-Based High-Fidelity Singing Voice Synthesis with Expressive Performance Control
Keywords:
singing voice synthesis; diffusion models; expressive performance control; data governance; audio generation; fairness; sustainabilityAbstract
Singing voice synthesis occupies a challenging intersection between linguistic intelligibility, musical pitch accuracy, timbral realism, and expressive nuance. Recent diffusion-based generative models have demonstrated the capacity to produce high-fidelity audio by iteratively transforming noise into structured waveforms or spectrograms. This paper presents a system-level investigation of diffusion-based singing voice synthesis with expressive performance control. Rather than focusing solely on model architecture, the study examines the structural trade-offs among acoustic fidelity, controllability, data requirements, inference cost, robustness, and fairness. The discussion begins with the motivational context of neural vocoding and text-to-speech, then develops a conceptual architecture in which score-aligned linguistic, pitch, duration, and performance descriptors condition a diffusion decoder. Expressive control is analyzed through conditioning mechanisms, reference encodings, and differentiable signal processing pathways that permit manipulation of vibrato, dynamics, breathiness, and articulation. The paper further addresses large-scale data infrastructure, annotation governance, evaluation protocols, and the socio-technical risks associated with voice synthesis. Deployment considerations include edge and cloud trade-offs, energy consumption, model provenance, and consent frameworks. The analysis highlights that high-fidelity singing voice synthesis is not solely a generative modeling problem but a multidimensional systems challenge. The paper concludes by identifying future directions in self-supervised representation learning, sustainable model design, and participatory governance. The integrated perspective is intended to support researchers and infrastructure builders in developing singing voice technologies that are technically robust and socially accountable.
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