  We propose an end-to-end music mixing style transfer system that converts the
mixing style of an input multitrack to that of a reference song. This is
achieved with an encoder pre-trained with a contrastive objective to extract
only audio effects related information from a reference music recording. All
our models are trained in a self-supervised manner from an already-processed
wet multitrack dataset with an effective data preprocessing method that
alleviates the data scarcity of obtaining unprocessed dry data. We analyze the
proposed encoder for the disentanglement capability of audio effects and also
validate its performance for mixing style transfer through both objective and
subjective evaluations. From the results, we show the proposed system not only
converts the mixing style of multitrack audio close to a reference but is also
robust with mixture-wise style transfer upon using a music source separation
model.
