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
Pages304-311
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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