New sonorities for jazz recordings: Separation and mixing using deep neural networks

The audio mixing process is an art that has proven to be extremely hard to model: What makes a certain mix better than another one? How can the mixing processing chain be automatically optimized to obtain better results in a more efficient manner? Over the last years, the scientific community has exploited methods from signal processing, music information retrieval, machine learning, and more recently, deep learning techniques to address these issues. In this work, a novel system based on deep neural networks (DNNs) is presented. It replaces the previously proposed steps of pitch-informed source separation and panoramabased remixing by an ensemble of trained DNNs.