Sound modelling is the process of developing algorithms that generate sound under parametric control. There are a few distinct approaches that have been developed historically including modelling the physics of sound production and propagation, assembling signal generating and processing elements to capture acoustic features, and manipulating collections of recorded audio samples. While each of these approaches has been able to achieve high-quality synthesis and interaction for specific applications, they are all labour-intensive and each comes with its own challenges for designing arbitrary control strategies. Recent generative deep learning systems for audio synthesis are able to learn models that can traverse arbitrary spaces of sound defined by the data they train on. Furthermore, machine learning systems are providing new techniques for designing control and navigation strategies for these models. This paper is a review of developments in deep learning that are changing the practice of sound modelling.

翻译：健全的建模是开发在参数控制下产生声音的算法的过程,在历史上已经发展了几种不同的方法,包括:对音响生产和传播的物理学进行建模,收集信号生成和处理要素以捕捉声学特征,对录音样本进行操纵收集,这些方法中的每一种都能够实现高质量的合成和具体应用的相互作用,但它们都是劳力密集型的,而且每个方法都面临着设计任意控制战略的挑战。最近,用于音频合成的基因化深层学习系统能够学习能够穿越它们所培训的数据所定义的任意音响空间的模型。此外,机器学习系统正在为这些模型设计控制和导航战略提供新技术。本文回顾了正在改变音响建模实践的深层次学习的发展情况。