Automatic mixing of musical compositions using machine learning
Date
2020
Authors
Blanckensee, Darren
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Abstract
Given the increasing availability of music production software and the many
live musical performances that take place without the assistance of professional
sound engineers, the need has arisen for automatic mixing software capable of
producing high quality mixes.
The mixing of songs is the process of going from individually recorded tracks
to a single song. This process involves the use of various e↵ects which are essentially
functions applied to the individual tracks in a particular order. After
the e↵ects have been applied to each of the tracks, the results are then added
together to form the final mix of the song. This project uses machine learning
to automate the mixing process. Although the mixing process generally
involves multiple e↵ects, this project considers only equalisation and volume
control. Two systems are designed, trained and tested, one for equalisation
and the other for volume control. Each system tests various architectures to
see which performs the best. The performances of the equalisation and volume
control systems are evaluated individually as well as together using a data set
of professionally mixed songs. The outputs of the neural networks are compared
with the professionally mixed songs from the dataset using spectrograms
and waveforms of the final mixes produced by each.
It is shown that by using well designed neural networks trained to a sufficient
level, the quality of the mixes produced by the neural networks approaches
that of the professionally mixed songs.
Description
A dissertation submitted in
fulfilment of the requirements for the degree of Master of
Science to the Faculty of Science, School of Computer Science
and Applied Mathematics, University of the Witwatersrand, 2020