Toward Expressive Timbre Modeling: Convergence of DDSP and Neural Voice Synthesis - Vaz, Henrique Maia Lins; Oliveira, João Pedro Mendes de (Anais do Simpósio Brasileiro de Computação Musical; 2025: Anais do XIX Simpósio Brasileiro de Computação Musical, 2025)
This article investigates the convergence between Differentiable Digital Signal Processing (DDSP) and neural singing voice synthesis approaches, focusing on the modeling and control of vocal timbre. By integrating explainable acoustic structures with deep learning, DDSP-based models offer interpretable, efficient, and expressive synthesis, overcoming limitations of purely neural architectures such as WaveNet or Tacotron. Analyzing models like the Neural Parametric Singing Synthesizer (NPSS), the article proposes a conceptual and technical framework that highlights recent advances in the explicit separation of pitch, timbre, and prosody. The study discusses implementations using differentiable LPC and FIR filters, hybrid vocoders, harmonic + noise synthesis, and strategies for timbre transfer and singing voice conversion (SVC). It argues that hybridization between DSP and neural networks marks a new methodological frontier for vocal synthesis — one that is more transparent, controllable, and suitable for expressive, pedagogical, and interactive applications.