The ubiquity of sound synthesizers has reshaped music production and even
entirely defined new music genres. However, the increasing complexity and
number of parameters in modern synthesizers make them harder to master. Hence,
the development of methods allowing to easily create and explore with
synthesizers is a crucial need. Here, we introduce a novel formulation of audio
synthesizer control. We formalize it as finding an organized latent audio space
that represents the capabilities of a synthesizer, while constructing an
invertible mapping to the space of its parameters. By using this formulation,
we show that we can address simultaneously automatic parameter inference,
macro-control learning and audio-based preset exploration within a single
model. To solve this new formulation, we rely on Variational Auto-Encoders
(VAE) and Normalizing Flows (NF) to organize and map the respective auditory
and parameter spaces. We introduce the disentangling flows, which allow to
perform the invertible mapping between separate latent spaces, while steering
the organization of some latent dimensions to match target variation factors by
splitting the objective as partial density evaluation. We evaluate our proposal
against a large set of baseline models and show its superiority in both
parameter inference and audio reconstruction. We also show that the model
disentangles the major factors of audio variations as latent dimensions, that
can be directly used as macro-parameters. We also show that our model is able
to learn semantic controls of a synthesizer by smoothly mapping to its
parameters. Finally, we discuss the use of our model in creative applications
and its real-time implementation in Ableton Live

本文介绍了一种基于变分自编码器和归一化流的新型音频合成器控制形式，以寻找音频合成器的组织潜在声音空间并构建可逆映射到其参数空间的方法，能够同时解决自动参数推断、宏控制学习和基于音频预设的探索问题。实验证明其在参数推断和音频重建方面的优越性，同时还能学习到合成器的语义控制，具有广泛的创意应用价值。