Sound synthesis, the generation of sound through analog electrical current or digital software, is a vibrant field since the second half of the 20th century. New synthesizers have expanded the scope of sounds that can be generated to previously unexplored reaches. But with this expansion also comes complexity: modern synthesizers, with sometimes over 100 continuous-valued parameters, can be daunting to program! Our work is an attempt at bridging the gap in sound synthesis between flexibility and ease of use. Thanks to recent advances in neural networks-based techniques, we provide users with the ability to edit and transform sounds through simple operations inspired by image processing software à-la-Paint. Namely, we frame the sound synthesis process as an interactive inpainting task, where portions of a sound are selectively transformed by the user. At each of these steps, our models are tasked with proposing new sonic content for the selected zones, by analyzing their surrounding context. In this talk, I will present both a novel machine learning architecture aimed at performing inpainting on spectrograms developed with Gaëtan Hadjeres (Sony CSL Paris), as well as a new, open-source interactive web interface that allows musicians to readily make use of these new models in creative settings: NOTONO.