A Digital Modeling Technique for Distortion Effect Based on a Machine Learning Approach
Yuto Matsunaga, Naofumi Aoki, Yoshinori Dobashi, Tsuyoshi Yamamoto · 2018
This paper describes an experimental result of modeling stomp boxes of the distortion effect based on a machine learning approach. Our proposed technique models the distortion stomp boxes as a neural network consisting of CNN and LSTM. In this approach, CNN is employed for modeling the linear component that appears in the pre and post filters of the stomp boxes. On the other hand, LSTM is employed for modeling the nonlinear component that appears in the distortion process of the stomp boxes. All the parameters are estimated through the training process using the input and output signals of the distortion stomp boxes. The experimental result indicates that the proposed technique may have a certain potential to replicate the distortion stomp boxes appropriately by using the well-trained neural network.