Abstract
Music source separation is the task of decomposing music into its constitutive components, e.g., yielding separated stems for the vocals, bass, and drums. Such a separation has many applications ranging from rearranging/repurposing the stems (remixing, repanning, upmixing) to full extraction (karaoke, sample creation, audio restoration). Music separation has a long history of scientific activity as it is known to be a very challenging problem. In recent years, deep learning-based systems-for the first time-yielded high-quality separations that also lead to increased commercial interest. However, until now, no open-source implementation that achieves state-of-the-art results is available. Open-Unmix closes this gap by providing a reference implementation based on deep neural networks. It serves two main purposes. Firstly, to accelerate academic research as Open-Unmix provides implementations for the most popular deep learning frameworks, giving researchers a flexible way to reproduce results. Secondly, we provide a pre-trained model for end users and even artists to try and use source separation. Furthermore, we designed Open-Unmix to be one core component in an open ecosystem on music separation, where we already provide open datasets, software utilities, and open evaluation to foster reproducible research as the basis of future development.
Highlights
Music separation is a problem which has fascinated researchers for over 50 years
Music source separation is the task of decomposing music into its constitutive components, e.g., yielding separated stems for the vocals, bass, and drums
Music separation has a long history of scientific activity as it is known to be a very challenging problem
Summary
Music separation is a problem which has fascinated researchers for over 50 years This is partly because, mathematically, there exists no closed-form solution when many sources (instruments) are recorded in a mono or stereo signal. For a more detailed overview see (Rafii, Liutkus, Stöter, Mimilakis, & Bittner, 2017) and (Cano, FitzGerald, Liutkus, Plumbley, & Stöter, 2019). Many of these methods were hand-crafted and tuned to a small number of music recordings (Araki et al, 2012; Ono, Koldovsky, Miyabe, & Ito, 2013; Vincent et al, 2012). Because commercial music is usually subject to copyright protection, and the separated stems are considered to be valuable assets in the music recording industry, they are usually unavailable
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