Grey-Box Modelling of Dynamic Range Compression
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A4 Artikkeli konferenssijulkaisussa
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2022
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en
Pages
8
304-311
304-311
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Proceedings of the 25th International Conference on Digital Audio Effects (DAFx20in22), issue 2022, Proceedings of the International Conference on Digital Audio Effects
Abstract
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.Description
Funding Information: ∗ This research belongs to the activities of the Nordic Sound and Music Computing Network-NordicSMC (NordForsk project number 86892). Publisher Copyright: Copyright: © 2022 Alec Wright et al.
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Wright, A & Välimäki, V 2022, Grey-Box Modelling of Dynamic Range Compression . in G Evangelista & N Holighaus (eds), Proceedings of the 25th International Conference on Digital Audio Effects (DAFx20in22) . 2022 edn, 35, Proceedings of the International Conference on Digital Audio Effects, DAFx, Vienna, Austria, pp. 304-311, International Conference on Digital Audio Effects, Vienna, Austria, 07/09/2022 . < https://dafx2020.mdw.ac.at/proceedings/papers/DAFx20in22_paper_35.pdf >