  Separating a song into vocal and accompaniment components is an active
research topic, and recent years witnessed an increased performance from
supervised training using deep learning techniques. We propose to apply the
visual information corresponding to the singers' vocal activities to further
improve the quality of the separated vocal signals. The video frontend model
takes the input of mouth movement and fuses it into the feature embeddings of
an audio-based separation framework. To facilitate the network to learn
audiovisual correlation of singing activities, we add extra vocal signals
irrelevant to the mouth movement to the audio mixture during training. We
create two audiovisual singing performance datasets for training and
evaluation, respectively, one curated from audition recordings on the Internet,
and the other recorded in house. The proposed method outperforms audio-based
methods in terms of separation quality on most test recordings. This advantage
is especially pronounced when there are backing vocals in the accompaniment,
which poses a great challenge for audio-only methods.
