利用GAN与集成未对齐干声数据改进无监督干声到渲染吉他音色转换
Improving Unsupervised Clean-to-Rendered Guitar Tone Transformation Using GANs and Integrated Unaligned Clean Data
AI总结：
针对吉他功放建模监督式方法数据对制作复杂、扩展性差的问题，本文在基于GAN的无监督框架中引入MSD与MPD两类判别器，并加入额外未配对干声数据训练，实验证明该改进对高低增益功放建模均有效。
摘要：
近年来，人们对将深度学习方法应用于吉他功放或效果器建模的兴趣日益浓厚。现有方法主要基于监督式方案，需要时间对齐的未处理音频与渲染音频数据对。但由于创建这类数据对的过程十分复杂，该方法的可扩展性不佳。Wright等人近期的一项研究探索了利用未配对数据进行训练的潜力，采用了基于生成对抗网络（GAN）的框架。本文对他们的工作进行了拓展，在GAN中使用了更先进的判别器，并采用更多未配对数据进行训练。具体而言，受神经声码器最新进展的启发，我们在用于吉他功放建模的GAN模型中采用了两组判别器，一组基于多尺度判别器（MSD），另一组基于多周期判别器（MPD）。此外，我们还尝试将不具备目标音色对应渲染音频的未处理音频信号加入训练数据，以探究GAN模型能从未配对数据中获得多少增益。我们的实验表明，上述两项拓展对低增益和高增益吉他功放的建模均有帮助。
英文摘要：
Recent years have seen increasing interest in applying deep learning methods to the modeling of guitar amplifiers or effect pedals. Existing methods are mainly based on the supervised approach, requiring temporally-aligned data pairs of unprocessed and rendered audio. However, this approach does not scale well, due to the complicated process involved in creating the data pairs. A very recent work done by Wright et al. has explored the potential of leveraging unpaired data for training, using a generative adversarial network (GAN)-based framework. This paper extends their work by using more advanced discriminators in the GAN, and using more unpaired data for training. Specifically, drawing inspiration from recent advancements in neural vocoders, we employ in our GAN-based model for guitar amplifier modeling two sets of discriminators, one based on multi-scale discriminator (MSD) and the other multi-period discriminator (MPD). Moreover, we experiment with adding unprocessed audio signals that do not have the corresponding rendered audio of a target tone to the training data, to see how much the GAN model benefits from the unpaired data. Our experiments show that the proposed two extensions contribute to the modeling of both low-gain and high-gain guitar amplifiers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
