  Due to the rapid development of deep learning, we can now successfully
separate singing voice from mono audio music. However, this separation can only
extract human voices from other musical instruments, which is undesirable for
karaoke content generation applications that only require the separation of
lead singers. For this karaoke application, we need to separate the music
containing male and female duets into two vocals, or extract a single lead
vocal from the music containing vocal harmony. For this reason, we propose in
this article to use a singer separation system, which generates karaoke content
for one or two separated lead singers. In particular, we introduced three
models for the singer separation task and designed an automatic model selection
scheme to distinguish how many lead singers are in the song. We also collected
a large enough data set, MIR-SingerSeparation, which has been publicly released
to advance the frontier of this research. Our singer separation is most
suitable for sentimental ballads and can be directly applied to karaoke content
generation. As far as we know, this is the first singer-separation work for
real-world karaoke applications.
