This paper describes a data-driven approach to creating real-time neural network models of guitar amplifiers, recreating the amplifiers' sonic response to arbitrary inputs at the full range of controls present on the physical device. While the focus on the paper is on the data collection pipeline, we demonstrate the effectiveness of this conditioned black-box approach by training an LSTM model to the task, and comparing its performance to an offline white-box SPICE circuit simulation. Our listening test results demonstrate that the neural amplifier modeling approach can match the subjective performance of a high-quality SPICE model, all while using an automated, non-intrusive data collection process, and an end-to-end trainable, real-time feasible neural network model.

翻译：本文描述了一种数据驱动的实时神经网络吉他放大器模型构建方法，该方法能够重现放大器对任意输入的声学响应，并涵盖物理设备上的全部控制参数范围。尽管本文重点关注数据采集流程，但通过训练LSTM模型完成该任务，并将其性能与离线白盒SPICE电路仿真进行对比，我们验证了这种条件化黑箱方法的有效性。听力测试结果表明，神经放大器建模方法能够达到高质量SPICE模型的主观性能水平，同时采用自动化、非侵入式数据采集流程，以及端到端可训练、实时可行的神经网络模型。