Open-Unmix - A Reference Implementation for Music Source Separation
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 manyapplications ranging from rearranging/repurposing the stems (remixing, repanning, upmixing)to full extraction (karaoke, sample creation, audio restoration). Music separation has a longhistory 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 alsolead to increased commercial interest. However, until now, no open-source implementationthat achieves state-of-the-art results is available.Open-Unmixcloses this gap by providinga reference implementation based on deep neural networks. It serves two main purposes.Firstly, to accelerate academic research asOpen-Unmixprovides implementations for themost 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 sourceseparation. Furthermore, we designedOpen-Unmixto be one core component in an openecosystem on music separation, where we already provide open datasets, software utilities,and open evaluation to foster reproducible research as the basis of future development.
Code (1)
Tasks
Music Source SeparationSimilar Papers 제목 키워드 기반
Transfer Learning with Jukebox for Music Source Separation
In this work, we demonstrate how a publicly available, pre-trained Jukebox model can be adapted for the problem of audio source separation from a single mixed audio channel. Our neural network architecture, which is usin…
Audio Source SeparationMusic Source SeparationTransfer LearningA Hands-on Comparison of DNNs for Dialog Separation Using Transfer Learning from Music Source Separation
This paper describes a hands-on comparison on using state-of-the-art music source separation deep neural networks (DNNs) before and after task-specific fine-tuning for separating speech content from non-speech content in…
Music Source SeparationTransfer LearningAll for One and One for All: Improving Music Separation by Bridging Networks
This paper proposes several improvements for music separation with deep neural networks (DNNs), namely a multi-domain loss (MDL) and two combination schemes. First, by using MDL we take advantage of the frequency and tim…
AllMusic Source SeparationMultitask learning for instrument activation aware music source separation
Music source separation is a core task in music information retrieval which has seen a dramatic improvement in the past years. Nevertheless, most of the existing systems focus exclusively on the problem of source separat…
Information RetrievalMusic Information RetrievalMusic Source SeparationRetrievalFast Semisupervised Unmixing Using Nonconvex Optimization
In this paper, we introduce a novel linear model tailored for semisupervised/library-based unmixing. Our model incorporates considerations for library mismatch while enabling the enforcement of the abundance sum-to-one c…