This paper introduces GlOttal-flow LPC Filter (GOLF), a novel method for singing voice synthesis (SVS) that exploits the physical characteristics of the human voice using differentiable digital signal processing. GOLF employs a glottal model as the harmonic source and IIR filters to simulate the vocal tract, resulting in an interpretable and efficient approach. We show it is competitive with state-of-the-art singing voice vocoders, requiring fewer synthesis parameters and less memory to train, and runs an order of magnitude faster for inference. Additionally, we demonstrate that GOLF can model the phase components of the human voice, which has immense potential for rendering and analysing singing voices in a differentiable manner. Our results highlight the effectiveness of incorporating the physical properties of the human voice mechanism into SVS and underscore the advantages of signal-processing-based approaches, which offer greater interpretability and efficiency in synthesis. Audio samples are available at https://yoyololicon.github.io/golf-demo/.

翻译：本文提出声门流LPC滤波器（GOLF），一种利用可微分数字信号处理技术、基于人体嗓音物理特性的新型歌声合成方法。GOLF采用声门模型作为谐波声源，并通过IIR滤波器模拟声道，从而形成可解释且高效的合成方案。实验表明，该方法与最先进的歌声声码器性能相当，但所需合成参数更少、训练内存更低，且推理速度提升一个数量级。此外，我们证明GOLF能够建模人声的相位分量，这为可微方式下的歌声渲染与分析展现了巨大潜力。研究结果凸显了将人体发声机制物理特性融入歌声合成的有效性，并彰显了基于信号处理方法在可解释性与合成效率方面的优势。音频示例请访问 https://yoyololicon.github.io/golf-demo/。