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Intelligent Technologies for Interactive Entertainment. 12th EAI International Conference, INTETAIN 2020, Virtual Event, December 12-14, 2020, Proceedings

Research Article

Modeling Audio Distortion Effects with Autoencoder Neural Networks

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  • @INPROCEEDINGS{10.1007/978-3-030-76426-5_9,
        author={Riccardo Russo and Francesco Bigoni and George Palamas},
        title={Modeling Audio Distortion Effects with Autoencoder Neural Networks},
        proceedings={Intelligent Technologies for Interactive Entertainment. 12th EAI International Conference, INTETAIN 2020, Virtual Event, December 12-14, 2020, Proceedings},
        proceedings_a={INTETAIN},
        year={2021},
        month={5},
        keywords={Autoencoders Convolutional autoencoders Audio distortion Audio effects modeling Black box modeling Machine learning for audio},
        doi={10.1007/978-3-030-76426-5_9}
    }
    
  • Riccardo Russo
    Francesco Bigoni
    George Palamas
    Year: 2021
    Modeling Audio Distortion Effects with Autoencoder Neural Networks
    INTETAIN
    Springer
    DOI: 10.1007/978-3-030-76426-5_9
Riccardo Russo,*, Francesco Bigoni, George Palamas
    *Contact email: rrusso19@student.aau.dk

    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.

    Keywords
    Autoencoders Convolutional autoencoders Audio distortion Audio effects modeling Black box modeling Machine learning for audio
    Published
    2021-05-19
    Appears in
    SpringerLink
    http://dx.doi.org/10.1007/978-3-030-76426-5_9
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