The development of intelligent music production tools has been of growing interest in recent years. Deep learning approaches have been shown as being a highly effective method for approximating individual audio effects. In this work, we propose an end-to-end deep neural network based on the Wave-U-Net to perform automatic mixing of drums. We follow an end-to-end approach where raw audio from the individual drum recordings is the input of the system and the waveform of the stereo mix is the output. We compare the system to existing machine learning approaches to intelligent drum mixing. Through a subjective listening test we explore the performance of these systems when processing various types of drum mixes. We report that the mixes generated by our model are virtually indistinguishable from professional human mixes while also outperforming previous intelligent mixing approaches.

Authors: Martínez Ramírez, Marco A.; Stoller, Daniel; Moffat, David
Affiliations: Centre for Digital Music, Queen Mary University of London, London, United Kingdom; Centre for Digital Music, Queen Mary University of London, London, United Kingdom; Interdisciplinary Center for Computer Music Research, University of Plymouth, Plymouth, United Kingdom(See document for exact affiliation information.)
JAES Volume 69 Issue 3 pp. 142-151; March 2021
Publication Date: March 9, 2021 Import into BibTeX
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=21023

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E-Library Location: (CD JAES69)   /jaes69/3/pg142.pdf

DOI: https://doi.org/10.17743/jaes.2020.0031

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