There is a body of work in the field of intelligent music production covering a range of specific audio effects. However, there is a distinct lack of any purely machine learning approaches to automatic mixing. This could be due to a lack of suitable data. This paper presents an approach to used human produced audio mixes, along with their source multitrack, to produce the set of mix parameters. The focus will be entirely on the gain mixing of audio drum tracks. Using existing reverse engineering of music production gain parameters, a target mix gain parameter is identified, and these results are fed into a number of machine learning algorithms, along with audio feature vectors of each audio track. This allow for a machine learning prediction approach to audio gain mixing. A random forest approach is taken to perform a multiple output prediction. The prediction results of the random forest approach are then compared to a number of other published automatic gain mixing approaches. The results demonstrate that the random forest gain mixing approach performs similarly to that of a human engineer and outperforms the existing gain mixing approaches.
(See document for exact affiliation information.)
https://aes2.org/publications/elibrary-page/?id=20550
(193KB)
Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member Join the AES. If you need to check your member status, login to the Member Portal.
