论文标题
使用变压器自动编码器的合成器预设插值
Synthesizer Preset Interpolation using Transformer Auto-Encoders
论文作者
论文摘要
声音合成器在现代音乐制作中广泛存在，但越来越多地需要掌握专家技能。这项工作着重于预设之间的插值，即所有声音综合参数的值集，以使现有声音的直观创建。 我们介绍了双峰自动编码器神经网络，该网络同时使用多头注意块进行预设，并使用卷积进行音频。该模型已在具有一百多个参数的流行频率调制合成器上进行了测试。实验将模型与相关的体系结构和方法进行了比较，并证明它执行了更平滑的插值。训练后，建议的模型可以集成到商业合成器中，以进行实时插值或声音设计任务。
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.
