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很普遍,但越来越需要掌握专家技能。 这项工作侧重于预设之间的内插,即所有声音合成参数的一组数值,以便能够从现有参数中直觉地创造出新的声音。 我们引入了双模式自动读数神经网络,它同时使用多头关注区块进行预设过程,并使用变幻器进行音频。这个模型已经在一个有一百多个参数的流行频率调制合成器上进行了测试。 实验将模型与相关结构和方法进行了比较,并表明它能够进行更平稳的内插。 经过培训,拟议的模型可以纳入商业合成器,用于现场的内插或声音设计任务。</s>