Sound synthesizers are ubiquitous in modern music production but manipulating their presets, i.e. the sets of synthesis parameters, demands expert skills. This study presents a novel variational auto-encoder model tailored for black-box synthesizer preset interpolation, which enables the intuitive generation of new presets from pre-existing ones. Leveraging multi-head self-attention networks, the model efficiently learns latent representations of synthesis parameters, aligning these with perceived timbre dimensions through attribute-based regularization. It is able to gradually transition between diverse presets, surpassing traditional linear parametric interpolation methods. Furthermore, we introduce an objective and reproducible evaluation method, based on linearity and smoothness metrics computed on a broad set of audio features. The model's efficacy is demonstrated through subjective experiments, whose results also highlight significant correlations with the proposed objective metrics. The model is validated using a widespread frequency modulation synthesizer with a large set of interdependent parameters. It can be adapted to various commercial synthesizers, and can perform other tasks such as modulations and extrapolations.

Sound synthesizers are widespread in modern music production but they increasingly require expert skills to be mastered. This work focuses on interpolation between presets, i.e., sets of values of all sound synthesis parameters, to enable the intuitive creation of new sounds from existing ones.We introduce a bimodal auto-encoder neural network, which simultaneously processes presets using multi-head attention blocks, and audio using convolutions. This model has been tested on a popular frequency modulation synthesizer with more than one hundred parameters. Experiments have compared the model to related architectures and methods, and have demonstrated that it performs smoother interpolations. After training, the proposed model can be integrated into commercial synthesizers for live interpolation or sound design tasks.

Deep neural networks have been recently applied to the task of automatic synthesizer programming, i.e., finding optimal values of sound synthesis parameters in order to reproduce a given input sound. This paper focuses on generative models, which can infer parameters as well as generate new sets of parameters or perform smooth morphing effects between sounds. We introduce new models to ensure scalability and to increase performance by using heterogeneous representations of parameters as numerical and categorical random variables. Moreover, a spectral variational autoencoder architecture with multi-channel input is proposed in order to improve inference of parameters related to the pitch and intensity of input sounds. Model performance was evaluated according to several criteria such as parameters estimation error and audio reconstruction accuracy. Training and evaluation were performed using a 30k presets dataset which is published with this paper. They demonstrate significant improvements in terms of parameter inference and audio accuracy and show that presented models can be used with subsets or full sets of synthesizer parameters.