  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/
