Abstrakt

The purpose of this dissertation is to develop an automatic song mixing system that is capable of automatically mixing a song with good quality in any music genre. This work recalls first the audio signal processing methods used in audio mixing, and it describes selected methods for automatic audio mixing. Then, a novel architecture built based on one-dimensional Wave-U-Net autoencoders is proposed for automatic music mixing. Models are trained on a custom-made database. Mixes created using the proposed system are compared with amateur, state-of-the-art software and professional mixes prepared by audio engineers. The achieved results prove that mixes created automatically by Wave-U-Net can objectively be evaluated as highly as mixes created professionally. This is also confirmed by the statistical analysis of the results of the conducted listening tests. 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 mix and the professional mix than the amateur mix or the mix prepared using state-of-the-art software. These results are also confirmed by the results of the similarity matrix-based analysis.

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