  Fundamental frequency (f0) modeling is an important but relatively unexplored
aspect of choir singing. Performance evaluation as well as auditory analysis of
singing, whether individually or in a choir, often depend on extracting f0
contours for the singing voice. However, due to the large number of singers,
singing at a similar frequency range, extracting the exact individual pitch
contours from choir recordings is a challenging task. In this paper, we address
this task and develop a methodology for modeling pitch contours of SATB choir
recordings. A typical SATB choir consists of four parts, each covering a
distinct range of pitches and often with multiple singers each. We first
evaluate some state-of-the-art multi-f0 estimation systems for the particular
case of choirs with a single singer per part, and observe that the pitch of
individual singers can be estimated to a relatively high degree of accuracy. We
observe, however, that the scenario of multiple singers for each choir part
(i.e. unison singing) is far more challenging. In this work we propose a
methodology based on combining a multi-f0 estimation methodology based on deep
learning followed by a set of traditional DSP techniques to model f0 and its
dispersion instead of a single f0 trajectory for each choir part. We present
and discuss our observations and test our framework with different singer
configurations.
