用物理模型引导神经合成，实现高质量且可控的声音生成。
Learning Control of Neural Sound Effects Synthesis from Physically Inspired Models
- 以物理启发模型为控制接口，驱动神经网络实时合成声音。
- 在保持高音质的同时，支持精细参数调控，优于传统方法。
- 适合需要精确控制声音效果的音频工程与游戏开发场景。
声音效果建模通常采用具有完全控制能力的数字信号处理技术，但在有限参数下难以实现逼真效果。近年来，神经声音合成方法展现出生成高质量、逼真声音的潜力，但难以对目标声音进行有效控制。本文提出一种基于物理启发模型的实时神经合成模型，既能生成高质量声音，又继承了物理模型的控制界面。实验表明，该模型在音质和控制性方面均表现优异。
原文摘要 · Abstract (English)
Sound effects model design commonly uses digital signal processing techniques with full control ability, but it is difficult to achieve realism within a limited number of parameters. Recently, neural sound effects synthesis methods have emerged as a promising approach for generating high-quality and realistic sounds, but the process of synthesizing the desired sound poses difficulties in terms of control. This paper presents a real-time neural synthesis model guided by a physically inspired model, enabling the generation of high-quality sounds while inheriting the control interface of the physically inspired model. We showcase the superior performance of our model in terms of sound quality and control.
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