In this paper, we present a new dataset of music performance videos which can be used for training machine learning methods for multiple tasks such as audio-visual blind source separation and localization, cross-modal correspondences, cross-modal generation and, in general, any audio-visual self-supervised task. These videos, gathered from YouTube, consist of solo musical performances of 13 different instruments. Compared to previously proposed audio-visual datasets, Solos is cleaner since a big amount of its recordings are auditions and manually checked recordings, ensuring there is no background noise nor effects added in the video post-processing. Besides, it is, up to the best of our knowledge, the only dataset that contains the whole set of instruments present in the URMP\cite{URPM} dataset, a high-quality dataset of 44 audio-visual recordings of multi-instrument classical music pieces with individual audio tracks. URMP was intented to be used for source separation, thus, we evaluate the performance on the URMP dataset of two different source-separation models trained on Solos. The dataset is publicly available at https://juanfmontesinos.github.io/Solos/

中文翻译：

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Solos：用于视听音乐分析的数据集

在本文中，我们提出了一个新的音乐表演视频数据集，可用于训练多任务机器学习方法，例如视听盲源分离和定位、跨模态对应、跨模态生成以及一般而言，任何视听自我监督任务。这些从 YouTube 收集的视频包含 13 种不同乐器的独奏音乐表演。与之前提出的视听数据集相比，Solos 更干净，因为它的大量录音是试听和手动检查的录音，确保没有背景噪音或视频后期处理中添加的效果。此外，据我们所知，它是唯一包含 URMP\cite{URPM} 数据集中存在的整套仪器的数据集，一个高质量的数据集，包含 44 个带有单独音轨的多乐器古典音乐作品的视听录音。URMP 旨在用于源分离，因此，我们评估了在 Solos 上训练的两种不同源分离模型在 URMP 数据集上的性能。该数据集可在 https://juanfmontesinos.github.io/Solos/ 公开获得