  Music source separation can be interpreted as the estimation of the
constituent music sources that a music clip is composed of. 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 propose Y-Net, an audio-visual
convolutional neural network which achieves state-of-the-art singing voice
separation results on the Acappella dataset and compare it against its
audio-only counterpart, U-Net, and a state-of-the-art audio-visual speech
separation model. Singing voice separation can be particularly challenging when
the audio mixture also comprises of other accompaniment voices and background
sounds along with the target voice of interest. We demonstrate that our model
can outperform the baseline models in the singing voice separation task in such
challenging scenarios. The code, the pre-trained models and the dataset will be
publicly available at https://ipcv.github.io/Acappella/
