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Neural Grey-Box Guitar Amplifier Modelling with Limited Data

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU148735" target="_blank" >RIV/00216305:26220/23:PU148735 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Neural Grey-Box Guitar Amplifier Modelling with Limited Data

  • Original language description

    This paper combines recurrent neural networks (RNNs) with the discretised Kirchhoff nodal analysis (DK-method) to create a grey-box guitar amplifier model. Both the objective and subjective results suggest that the proposed model is able to outperform a baseline black-box RNN model in the task of modelling a guitar amplifier, including realistically recreating the behaviour of the amplifier equaliser circuit, whilst requiring significantly less training data. Furthermore, we adapt the linear part of the DK-method in a deep learning scenario to derive multiple state-space filters simultaneously. We frequency sample the filter transfer functions in parallel and perform frequency domain filtering to considerably reduce the required training times compared to recursive state-space filtering. This study shows that it is a powerful idea to separately model the linear and nonlinear parts of a guitar amplifier using supervised learning.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20203 - Telecommunications

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of the 25th International Conference on Digital Audio Effects (DAFx23)

  • ISBN

  • ISSN

    2413-6689

  • e-ISSN

  • Number of pages

    8

  • Pages from-to

    „“-„“

  • Publisher name

    Aalborg University of Copenhagen

  • Place of publication

    Kodaň

  • Event location

    Kodaň

  • Event date

    Sep 4, 2023

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article