A Black Box Modeling Technique for Distortion Stomp Boxes Using LSTM Neural Networks
| Autor: | Naofumi Aoki, Yuto Matsunaga, Tetsuya Kojima, Yoshinori Dobashi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Black box (phreaking)
0209 industrial biotechnology Artificial neural network Computer science Data_MISCELLANEOUS Process (computing) 02 engineering and technology Nonlinear system 020901 industrial engineering & automation Nonlinear distortion Distortion 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Algorithm |
| Zdroj: | ICAIIC |
| DOI: | 10.1109/icaiic48513.2020.9065277 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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