Music mixing traditionally involves recording instruments in the form of clean, individual tracks and blending them into a final mixture using audio effects and expert knowledge (e.g., a mixing engineer). The automation of music production tasks has become an emerging field in recent years, where rule-based methods and machine learning approaches have been explored. Nevertheless, the lack of dry or clean instrument recordings limits the performance of such models, which is still far from professional human-made mixes. We explore whether we can use out-of-domain data such as wet or processed multitrack music recordings and repurpose it to train supervised deep learning models that can bridge the current gap in automatic mixing quality. To achieve this we propose a novel data preprocessing method that allows the models to perform automatic music mixing. We also redesigned a listening test method for evaluating music mixing systems. We validate our results through such subjective tests using highly experienced mixing engineers as participants.

翻译：传统上,音乐混合是指以清洁、单曲形式录制乐器,并用音效和专业知识(例如混合工程师)将其混合成最后混合。近年来,音乐生产任务的自动化已成为一个新领域,探索了基于规则的方法和机器学习方法。然而,由于缺乏干或清洁的仪器记录,这些模型的性能仍然远非专业的人类制作混合。我们探讨我们是否可以使用诸如湿或处理过的多轨音乐录音等外部数据,并重新利用这些数据来培训有监督的深层次学习模型,以弥补目前自动混合质量方面的差距。为此,我们提出一种新的数据预处理方法,使模型能够进行自动音乐混合。我们还重新设计了一种用于评价音乐混合系统的监听测试方法。我们用经验丰富的混合工程师作为参与者,通过这种主观测试来验证我们的成果。