The sigsep musdb18 data set consists of a total of 150 full-track songs of different styles and includes both the stereo mixtures and the original sources, divided between a training subset and a test subset.
\n\nIts purpose is to serve as a reference database for the design and the evaluation of source separation algorithms. The objective of such signal processing methods is to estimate one or more sources from a set of mixtures, e.g. for karaoke applications. It has been used as the official dataset in the professionally-produced music recordings task for SiSEC 2018, which is the international campaign for the evaluation of source separation algorithms.
\n\nmusdb18 contains two folders, a folder with a training set: “train”, composed of 100 songs, and a folder with a test set: “test”, composed of 50 songs. Supervised approaches should be trained on the training set and tested on both sets.
\n\nAll files from the musdb18 dataset are encoded in the Native Instruments stems format (.mp4). It is a multitrack format composed of 5 stereo streams, each one encoded in AAC @256kbps. These signals correspond to:
\n\nFor each file, the mixture correspond to the sum of all the signals. All signals are stereophonic and encoded at 44.1kHz.
\n\nAs the MUSDB18 is encoded as STEMS, it relies on ffmpeg to read the multi-stream files. We provide a python wrapper called stempeg that allows to easily parse the dataset and decode the stem tracks on-the-fly.
\n\nLicense
\n\nMUSDB18 is provided for educational purposes only and the material contained in them should not be used for any commercial purpose without the express permission of the copyright holders:
\n\nReferences
\n\nIf you use the MUSDB dataset for your research - Cite the MUSDB18 Dataset
\n\n@misc{MUSDB18,\n author = {Rafii, Zafar and\n Liutkus, Antoine and\n Fabian-Robert St{\\\"o}ter and\n Mimilakis, Stylianos Ioannis and\n Bittner, Rachel},\n title = {The {MUSDB18} corpus for music separation},\n month = dec,\n year = 2017,\n doi = {10.5281/zenodo.1117372},\n url = {https://doi.org/10.5281/zenodo.1117372}\n}\n\nIf compare your results with SiSEC 2018 Participants - Cite the SiSEC 2018 LVA/ICA Paper
\n\n@inproceedings{SiSEC18,\n author=\"St{\\\"o}ter, Fabian-Robert and Liutkus, Antoine and Ito, Nobutaka\",\n title=\"The 2018 Signal Separation Evaluation Campaign\",\n booktitle=\"Latent Variable Analysis and Signal Separation:\n 14th International Conference, LVA/ICA 2018, Surrey, UK\",\n year=\"2018\",\n pages=\"293--305\"\n}\n\n
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