  Singing voice separation (SVS) is a task that separates singing voice audio
from its mixture with instrumental audio. Previous SVS studies have mainly
employed the spectrogram masking method which requires a large dimensionality
in predicting the binary masks. In addition, they focused on extracting a vocal
stem that retains the wet sound with the reverberation effect. This result may
hinder the reusability of the isolated singing voice. This paper addresses the
issues by predicting mel-spectrogram of dry singing voices from the mixed audio
as neural vocoder features and synthesizing the singing voice waveforms from
the neural vocoder. We experimented with two separation methods. One is
predicting binary masks in the mel-spectrogram domain and the other is directly
predicting the mel-spectrogram. Furthermore, we add a singing voice detector to
identify the singing voice segments over time more explicitly. We measured the
model performance in terms of audio, dereverberation, separation, and overall
quality. The results show that our proposed model outperforms state-of-the-art
singing voice separation models in both objective and subjective evaluation
except the audio quality.
