Modeling Audio Distortion Effects with Autoencoder Neural Networks

Most music production nowadays is carried out using software tools: for this reason, the market demands faithful audio effect simulations. Traditional methods for modeling nonlinear systems are effect-specific or labor-intensive; however, recent works yielded promising results by black-box simulation of these effects using neural networks. This work aims to explore two models of distortion effects based on autoencoders: one makes use of fully-connected layers only, and the other employs convolutional layers. Both models were trained using clean sounds as input and distorted sounds as target, thus, the learning method was not self-supervised, as it is mostly the case when dealing with autoencoders. The networks were then tested with visual inspection of the output spectrograms, as well as with an informal listening test, and performed well in reconstructing the distorted signal spectra, however a fair amount of noise was also introduced.