MUSDB18-HQ - an uncompressed version of MUSDB18

MUSDB18-HQ is the uncompressed version of the MUSDB18 dataset. It 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.

Its 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.

musdb18-hq 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.

All files from the musdb18-hq dataset are saved as uncompressed wav files. Within each track folder, the user finds

mixture.wav

drums.wav

bass.wav,

other.wav,

vocals.wav

All signals are stereophonic and encoded at 44.1kHz.

LICENSE

MUSDBHQ: 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:

    100 tracks are taken from the DSD100 data set, which is itself derived from The ‘Mixing Secrets’ Free Multitrack Download Library. Please refer to this original resource for any question regarding your rights on your use of the DSD100 data.
    46 tracks are taken from the MedleyDB licensed under Creative Commons (BY-NC-SA 4.0).
    2 tracks were kindly provided by Native Instruments originally part of their stems pack.
    2 tracks a from from the Canadian rock band The Easton Ellises as part of the heise stems remix competition, licensed under Creative Commons (BY-NC-SA 3.0).

REFERENCE

If you use the MUSDB dataset for your research - Cite the MUSDB18 Dataset

@misc{MUSDB18HQ,
author = {Rafii, Zafar and
Liutkus, Antoine and
Fabian-Robert St{\"o}ter and
Mimilakis, Stylianos Ioannis and
Bittner, Rachel},
title = {{MUSDB18-HQ} - an uncompressed version of MUSDB18},
month = dec,
year = 2019,
doi = {10.5281/zenodo.3338373},
url = {https://doi.org/10.5281/zenodo.3338373}
}

If compare your results with SiSEC 2018 Participants - Cite the SiSEC 2018 LVA/ICA Paper

@inproceedings{SiSEC18,
author="St{\"o}ter, Fabian-Robert and Liutkus, Antoine and Ito, Nobutaka",
title="The 2018 Signal Separation Evaluation Campaign",
booktitle="Latent Variable Analysis and Signal Separation:
14th International Conference, LVA/ICA 2018, Surrey, UK",
year="2018",
pages="293--305"
}

MUSDB18-HQ 是 MUSDB18 数据集的未压缩版本。该数据集总计包含150首不同风格的完整曲目，同时提供立体声混合音频与原始分离声源，并划分为训练子集与测试子集。

其设计目标是作为声源分离算法研发与评估的基准数据库。此类信号处理方法的核心任务是从一组混合音频中估计一个或多个目标声源，例如应用于卡拉OK场景。它曾作为2018年国际声源分离算法评估赛事（SiSEC 2018）中专业音乐录制任务的官方数据集。

musdb18-hq 包含两个文件夹：训练集文件夹“train”（包含100首曲目）与测试集文件夹“test”（包含50首曲目）。有监督学习方法应在训练集上完成模型训练，并在训练集与测试集两个子集上进行性能测试。

musdb18-hq 中的所有文件均以未压缩WAV格式存储。在每个曲目文件夹中，用户可找到以下音频文件：
mixture.wav（混合音频）、drums.wav（鼓声）、bass.wav（贝斯声部）、other.wav（其他声部）、vocals.wav（人声）。
所有音频均为立体声编码，采样率为44.1kHz。

LICENSE

MUSDBHQ 仅用于教育目的，未经版权所有者明确许可，不得将其中包含的素材用于任何商业用途：
1. 100首曲目取自DSD100数据集，而DSD100数据集本身源自《Mixing Secrets》免费多轨素材下载库。若您对使用DSD100数据集的相关权利有疑问，请参阅该原始资源。
2. 46首曲目取自MedleyDB，该数据集采用知识共享（BY-NC-SA 4.0）许可协议。
3. 2首曲目由Native Instruments慷慨提供，最初属于其Stems素材包的一部分。
4. 2首曲目来自加拿大摇滚乐队The Easton Ellises，作为heise stems混音大赛的参赛作品，采用知识共享（BY-NC-SA 3.0）许可协议。

REFERENCE

若您在研究中使用MUSDB数据集，请引用MUSDB18数据集：
@misc{MUSDB18HQ,
author = {Rafii, Zafar and
Liutkus, Antoine and
Fabian-Robert St{"o}ter and
Mimilakis, Stylianos Ioannis and
Bittner, Rachel},
title = {{MUSDB18-HQ} - an uncompressed version of MUSDB18},
month = dec,
year = 2019,
doi = {10.5281/zenodo.3338373},
url = {https://doi.org/10.5281/zenodo.3338373}
}

若您的研究结果需与SiSEC 2018参赛作品进行对比，请引用SiSEC 2018 LVA/ICA会议论文：
@inproceedings{SiSEC18,
author="St{"o}ter, Fabian-Robert and Liutkus, Antoine and Ito, Nobutaka",
title="The 2018 Signal Separation Evaluation Campaign",
booktitle="Latent Variable Analysis and Signal Separation:
14th International Conference, LVA/ICA 2018, Surrey, UK",
year="2018",
pages="293--305"
}