  We propose a data cleansing method that utilizes a neural analysis and
synthesis (NANSY++) framework to train an end-to-end neural diarization model
(EEND) for singer diarization. Our proposed model converts song data with
choral singing which is commonly contained in popular music and unsuitable for
generating a simulated dataset to the solo singing data. This cleansing is
based on NANSY++, which is a framework trained to reconstruct an input
non-overlapped audio signal. We exploit the pre-trained NANSY++ to convert
choral singing into clean, non-overlapped audio. This cleansing process
mitigates the mislabeling of choral singing to solo singing and helps the
effective training of EEND models even when the majority of available song data
contains choral singing sections. We experimentally evaluated the EEND model
trained with a dataset using our proposed method using annotated popular duet
songs. As a result, our proposed method improved 14.8 points in diarization
error rate.
