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.

翻译：声音合成器在现代音乐制作中广泛应用，但日益需要专家技能才能掌握。本研究聚焦于预设值（即所有声音合成参数的数值集合）之间的插值，旨在实现从现有声音直觉式创建新声音。我们提出了一种双模态自编码器神经网络，该网络通过多头注意力模块同时处理预设值，并利用卷积处理音频。该模型已在一款拥有百余个参数的流行频率调制合成器上进行了测试。实验将所提模型与相关架构及方法进行了对比，证明其能实现更平滑的插值。训练完成后，该模型可集成至商业合成器中，用于实时插值或声音设计任务。