Modeling Audio Distortion Effects with Autoencoder Neural Networks

Riccardo Russo, Francesco Bigoni, George Palamas

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationIntelligent Technologies for Interactive Entertainment : 12th EAI International Conference, INTETAIN 2020
Volume377
PublisherSpringer
Publication date2021
Pages131-141
ISBN (Print)978-3-030-76425-8
ISBN (Electronic)978-3-030-76426-5
DOIs
Publication statusPublished - 2021
EventEAI Intetain 2020 – 12th EAI International Conference on Intelligent Technologies for Interactive Entertainment - Online, Santa Clara, United States
Duration: 12 Dec 202014 Dec 2020
https://intetain.eai-conferences.org/2020/

Conference

ConferenceEAI Intetain 2020 – 12th EAI International Conference on Intelligent Technologies for Interactive Entertainment
LocationOnline
Country/TerritoryUnited States
CitySanta Clara
Period12/12/202014/12/2020
Internet address
SeriesLecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Volume377
ISSN1867-8211

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