| Title | A Black Box Modeling Technique for Distortion Stomp Boxes Using LSTM Neural Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | MATSUNAGA, Y., AOKI, N., DOBASHI, Y., KOJIMA, T. |
| Conference Name | 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) |
| Keywords | black box encryption, black box modeling, commercial stomp boxes, composability, correlation coefficient, distortion sound, distortion stomp box, Integrated circuit modeling, learning (artificial intelligence), LSTM, LSTM neural networks, machine learning, Metrics, Neural Network, Neural networks, Nonlinear distortion, nonlinear systems, pubcrawl, recurrent neural nets, Resiliency, sound effect, stomp box, Training |
| Abstract | This paper describes an experimental result of modeling stomp boxes of the distortion effect based on a machine learning approach. Our proposed technique models a distortion stomp box as a neural network consisting of LSTM layers. In this approach, the neural network is employed for learning the nonlinear behavior of the distortion stomp boxes. All the parameters for replicating the distortion sound are estimated through its training process using the input and output signals obtained from some commercial stomp boxes. The experimental result indicates that the proposed technique may have a certain appropriateness to replicate the distortion sound by using the well-trained neural networks. |
| DOI | 10.1109/ICAIIC48513.2020.9065277 |
| Citation Key | matsunaga_black_2020 |
A Black Box Modeling Technique for Distortion Stomp Boxes Using LSTM Neural Networks