Grey-Box Modelling of Dynamic Range Compression
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A4 Artikkeli konferenssijulkaisussa
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en
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8
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Proceedings of the 25th International Conference on Digital Audio Effects (DAFx20in22), issue 2022, pp. 304-311, 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 >