论文标题
Drumgan VST：使用自动编码生成对抗网络的鼓声分析/合成的插件
DrumGAN VST: A Plugin for Drum Sound Analysis/Synthesis With Autoencoding Generative Adversarial Networks
论文作者
论文摘要
在当代流行的音乐作品中，鼓声设计通常是由繁琐的浏览和处理声音库中预录的样品的处理。人们还可以使用专门的合成硬件，通常通过低级，音乐上毫无意义的参数来控制。如今，深度学习领域提供了通过学习的高级功能来控制综合过程的方法，并允许产生各种声音。在本文中，我们提出了Drumgan VST，这是一个使用生成对抗网络合成鼓声的插件。 Drumgan VST可在44.1 kHz样品速率音频上运行，提供独立且连续的仪表类控件，并具有编码的神经网络，该神经网络映射到GAN的潜在空间中，从而可以重新合成并操纵持续的鼓声。我们提供了许多声音示例和建议的VST插件的演示。
In contemporary popular music production, drum sound design is commonly performed by cumbersome browsing and processing of pre-recorded samples in sound libraries. One can also use specialized synthesis hardware, typically controlled through low-level, musically meaningless parameters. Today, the field of Deep Learning offers methods to control the synthesis process via learned high-level features and allows generating a wide variety of sounds. In this paper, we present DrumGAN VST, a plugin for synthesizing drum sounds using a Generative Adversarial Network. DrumGAN VST operates on 44.1 kHz sample-rate audio, offers independent and continuous instrument class controls, and features an encoding neural network that maps sounds into the GAN's latent space, enabling resynthesis and manipulation of pre-existing drum sounds. We provide numerous sound examples and a demo of the proposed VST plugin.
