  This paper addresses the extraction of multiple F0 values from polyphonic and
a cappella vocal performances using convolutional neural networks (CNNs). We
address the major challenges of ensemble singing, i.e., all melodic sources are
vocals and singers sing in harmony. We build upon an existing architecture to
produce a pitch salience function of the input signal, where the harmonic
constant-Q transform (HCQT) and its associated phase differentials are used as
an input representation. The pitch salience function is subsequently
thresholded to obtain a multiple F0 estimation output. For training, we build a
dataset that comprises several multi-track datasets of vocal quartets with F0
annotations. This work proposes and evaluates a set of CNNs for this task in
diverse scenarios and data configurations, including recordings with additional
reverb. Our models outperform a state-of-the-art method intended for the same
music genre when evaluated with an increased F0 resolution, as well as a
general-purpose method for multi-F0 estimation. We conclude with a discussion
on future research directions.
