Automatic music signal mixing system based on one-dimensional Wave-U-Net autoencoders

Abstract

Abstract The purpose of this paper is to show a music mixing system that is capable of automatically mixing separate raw recordings with good quality regardless of the music genre. This work recalls selected methods for automatic audio mixing first. Then, a novel deep model based on one-dimensional Wave-U-Net autoencoders is proposed for automatic music mixing. The model is trained on a custom-prepared database. Mixes created using the proposed system are compared with amateur, state-of-the-art software, and professional mixes prepared by audio engineers. The results obtained prove that mixes created automatically by Wave-U-Net can objectively be evaluated as highly as mixes prepared professionally. This is also confirmed by the statistical analysis of the results of the conducted listening tests. Moreover, the results show a strong correlation between the experience of the listeners in mixing and the likelihood of a higher rating of the Wave-U-Net-based and professional mixes than the amateur ones or the mix prepared using state-of-the-art software. These results are also confirmed by the outcome of the similarity matrix-based analysis.

Keywords

• Automatic music mixing
• Wave-U-Net autoencoder
• Music signal parameterization
• Listening tests
• Similarity matrix