文章基本信息

标题：Real Time Emulation of Parametric Guitar Tube Amplifier with Long Short Term Memory Neural Network
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作者：Thomas Schmitz ; Jean-Jacques Embrechts
期刊名称：Computer Science & Information Technology
电子版ISSN：2231-5403
出版年度：2018
卷号：8
期号：5
页码：149-157
DOI：10.5121/csit.2018.80511
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Numerous audio systems for musicians are expensive and bulky. Therefore, it could beadvantageous to model them and to replace them by computer emulation. In guitar players’world, audio systems could have a desirable nonlinear behavior (distortion effects). It is thusdifficult to find a simple model to emulate them in real time. Volterra series model and itssubclass are usual ways to model nonlinear systems. Unfortunately, these systems are difficultto identify in an analytic way. In this paper we propose to take advantage of the new progressmade in neural networks to emulate them in real time. We show that an accurate emulation canbe reached with less than 1% of root mean square error between the signal coming from a tubeamplifier and the output of the neural network. Moreover, the research has been extended tomodel the Gain parameter of the amplifier.
关键词：Tube Amplifiers; Nonlinear Systems; Neural-Network; Real-Time.