论文标题
具有未配对数据的对抗吉他放大器建模
Adversarial Guitar Amplifier Modelling With Unpaired Data
论文作者
论文摘要
我们提出了一个音频效果处理框架，该框架学会从录音中模仿目标电吉他音调。我们使用对抗性方法训练深层神经网络，目的是将吉他的音色转换为其他吉他的音色，例如在音频效果处理后，例如，吉他放大器应用了。模型培训不需要配对的数据，因此产生的模型可以很好地模拟目标音色，同时可以在现代的个人计算机上实时处理。为了验证我们的方法，我们提出了两个实验，一个实验可以使用配对数据进行未配对的培训，使我们能够通过目标指标监视培训，另一个使用完全未配对的数据进行监视，与用户希望仅使用录音中的音频数据模仿吉他的吉他小弹力。我们的听力测试结果证实了这些模型在感知上令人信服。
We propose an audio effects processing framework that learns to emulate a target electric guitar tone from a recording. We train a deep neural network using an adversarial approach, with the goal of transforming the timbre of a guitar, into the timbre of another guitar after audio effects processing has been applied, for example, by a guitar amplifier. The model training requires no paired data, and the resulting model emulates the target timbre well whilst being capable of real-time processing on a modern personal computer. To verify our approach we present two experiments, one which carries out unpaired training using paired data, allowing us to monitor training via objective metrics, and another that uses fully unpaired data, corresponding to a realistic scenario where a user wants to emulate a guitar timbre only using audio data from a recording. Our listening test results confirm that the models are perceptually convincing.
