## 1 Introduction  
Synthesizers are parametric systems able to generate audio signals ranging from musical instruments to entirely unheard-of sound textures. Since their commercial beginnings more than 50 years ago, synthesizers have revolutionized music production, while becoming increasingly accessible, even to neophytes with no background in signal processing. While there exists a variety of sound synthesis types [1], all of these techniques require an extensive a priori knowledge to make the most out of a synthesizer possibilities. Hence, the main appeal of these systems (namely their versatility provided by large sets of parameters) also entails their major drawback. Indeed, the sheer combinatorics of parameter settings makes exploring all possibilitiesto find an adequate sound a daunting and time-consuming task. Furthermore, there exist highly non-linear relationships between the parameters and the resulting audio. Unfortunately, no synthesizer provides intuitive controls related to perceptual and semantic properties of the generated audio. Hence, a method allowing an intuitive and creative exploration of sound synthesizers has become a crucial need, especially for non-expert users. A potential direction taken by synth manufacturers is to propose programmable _macro-controls_ that allow to efficiently manipulate the generated sound qualities by controlling multiple parameters through a single knob. However, these need to be programmed manually, which still requires expert knowledge. Furthermore, no method has ever tried to tackle this _macro-control learning_ task, as this objective appears unclear and depends on a variety of unknown factors. An alternative to manual parameters setting would be to infer the set of parameters that could best reproduce a given _target sound_. This task of _parameters inference_ has been studied in the past years using various techniques, such as iterative relevance feedback on audio descriptors [2], Genetic Programming to directly grow modular synthesizers [3], or bi-directional LSTM with highway layers [4] to produce parameters approximation. Although these approaches might be appealing, they all share the same fundamental flaws that (i) though it is unlikely that a synthesizer can generate exactly any audio target, none explicitly model these limitations, (ii) they do not account for the non-linear relationships that exist between parameters and the corresponding synthesized audio, and (iii) none of these approaches allow for higher-level controls or interaction with audio synthesizers. Hence, no approach has succeeded in unveiling the true relationships between these _auditory_ and _parameters_ spaces. Hence, it appears mandatory to organize the parameters and audio capabilities of a given synthesizer in their respective spaces, while constructing an invertible mapping between these spaces in order to access a range of high-level interactions. This idea is depicted in Figure 1.  
The recent rise of _generative models_ might provide an elegant solution to these questions. Indeed, amongst these models, the _Variational Auto-Encoder_ (VAE) [5] aims to uncover the underlying structure of the data, by explicitly learning a _latent space_[5]. This space can be seen as a high-level representation, which aims to disentangle underlying variation factors and reveal interesting structural properties of the data [5,6]. VAEs address the limitations of control and analysis through this latent space, while being able to learn on small sets of examples. Furthermore, the recently proposed _Normalizing  
Figure 1: _Universal synthesizer control_. (**a**) Previous methods perform direct parameter inference from the audio, which is inherently limited by the non-differentiable synthesis operation and provides no higher-level form of control. (**b**) Our novel formulation states that we should first learn an organized and compressed latent space **z** of the synthesizer’s audio capabilities, while mapping it to the space **v** of its synthesis parameters.