  Choral singing is a widely practiced form of ensemble singing wherein a group
of people sing simultaneously in polyphonic harmony. The most commonly
practiced setting for choir ensembles consists of four parts; Soprano, Alto,
Tenor and Bass (SATB), each with its own range of fundamental frequencies
(F$0$s). The task of source separation for this choral setting entails
separating the SATB mixture into the constituent parts. Source separation for
musical mixtures is well studied and many deep learning based methodologies
have been proposed for the same. However, most of the research has been focused
on a typical case which consists in separating vocal, percussion and bass
sources from a mixture, each of which has a distinct spectral structure. In
contrast, the simultaneous and harmonic nature of ensemble singing leads to
high structural similarity and overlap between the spectral components of the
sources in a choral mixture, making source separation for choirs a harder task
than the typical case. This, along with the lack of an appropriate consolidated
dataset has led to a dearth of research in the field so far. In this paper we
first assess how well some of the recently developed methodologies for musical
source separation perform for the case of SATB choirs. We then propose a novel
domain-specific adaptation for conditioning the recently proposed U-Net
architecture for musical source separation using the fundamental frequency
contour of each of the singing groups and demonstrate that our proposed
approach surpasses results from domain-agnostic architectures.
