Grey-box modelling of dynamic range compression

Alec Wright, Vesa Valimaki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

This paper explores the digital emulation of analog dynamic range compressors, proposing a grey-box model that uses a combination of traditional signal processing techniques and machine learning. The main idea is to use the structure of a traditional digital compressor in a machine learning framework, so it can be trained end-to-end to create a virtual analog model of a compressor from data. The complexity of the model can be adjusted, allowing a trade-off between the model accuracy and computational cost. The proposed model has interpretable components, so its behaviour can be controlled more readily after training in comparison to a black-box model. The result is a model that achieves similar accuracy to a black-box baseline, whilst requiring less than 10% of the number of operations per sample at runtime.
Original languageEnglish
Title of host publicationProceedings of the 25th International Conference on Digital Audio Effects (DAFx20in22)
Place of PublicationVienna, Austria
Number of pages8
Publication statusPublished - 6 Sept 2022

Publication series

NameProceedings of the International Conference on Digital Audio Effects
ISSN (Print)2413-6700
ISSN (Electronic)2413-6689


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