论文标题
独奏：用于视听音乐分析的数据集
Solos: A Dataset for Audio-Visual Music Analysis
论文作者
论文摘要
在本文中，我们介绍了一个新的音乐性能视频数据集，可用于训练机器学习方法，以用于多个任务，例如视听盲目的源分离和本地化，跨模式对应关系，跨模式生成，以及通常的任何音频视频自我审查的任务。这些视频是从YouTube收集的，由13种不同乐器的独奏音乐表演组成。与先前提出的视听数据集相比，独奏更清洁，因为其大量录音是试镜和手动检查录音，从而确保视频后处理中没有添加背景噪声或效果。此外，据我们所知，这是唯一包含URMP \ cite {urpm}数据集中的整个仪器集的数据集，这是一个高质量的数据集，该数据集由44个带有单个autio轨道的多件音乐录音的44个音频录音。 URMP的目的是用于源分离，因此，我们评估了在独奏中训练的两个不同源分离模型的URMP数据集上的性能。该数据集可在https://juanfmontesinos.github.io/solos/上公开获取
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/
