  Separation of multiple singing voices into each voice is a rarely studied
area in music source separation research. The absence of a benchmark dataset
has hindered its progress. In this paper, we present an evaluation dataset and
provide baseline studies for multiple singing voices separation. First, we
introduce MedleyVox, an evaluation dataset for multiple singing voices
separation. We specify the problem definition in this dataset by categorizing
it into i) unison, ii) duet, iii) main vs. rest, and iv) N-singing separation.
Second, to overcome the absence of existing multi-singing datasets for a
training purpose, we present a strategy for construction of multiple singing
mixtures using various single-singing datasets. Third, we propose the improved
super-resolution network (iSRNet), which greatly enhances initial estimates of
separation networks. Jointly trained with the Conv-TasNet and the multi-singing
mixture construction strategy, the proposed iSRNet achieved comparable
performance to ideal time-frequency masks on duet and unison subsets of
MedleyVox. Audio samples, the dataset, and codes are available on our website
(https://github.com/jeonchangbin49/MedleyVox).
