  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.
