  The task of isolating a target singing voice in music videos has useful
applications. In this work, we explore the single-channel singing voice
separation problem from a multimodal perspective, by jointly learning from
audio and visual modalities. To do so, we present Acappella, a dataset spanning
around 46 hours of a cappella solo singing videos sourced from YouTube. We also
propose an audio-visual convolutional network based on graphs which achieves
state-of-the-art singing voice separation results on our dataset and compare it
against its audio-only counterpart, U-Net, and a state-of-the-art audio-visual
speech separation model. We evaluate the models in the following challenging
setups: i) presence of overlapping voices in the audio mixtures, ii) the target
voice set to lower volume levels in the mix, and iii) combination of i) and
ii). The third one being the most challenging evaluation setup. We demonstrate
that our model outperforms the baseline models in the singing voice separation
task in the most challenging evaluation setup. The code, the pre-trained
models, and the dataset are publicly available at
https://ipcv.github.io/Acappella/able at https://ipcv.github.io/Acappella/
