  We propose a flexible framework that deals with both singer conversion and
singers vocal technique conversion. The proposed model is trained on
non-parallel corpora, accommodates many-to-many conversion, and leverages
recent advances of variational autoencoders. It employs separate encoders to
learn disentangled latent representations of singer identity and vocal
technique separately, with a joint decoder for reconstruction. Conversion is
carried out by simple vector arithmetic in the learned latent spaces. Both a
quantitative analysis as well as a visualization of the converted spectrograms
show that our model is able to disentangle singer identity and vocal technique
and successfully perform conversion of these attributes. To the best of our
knowledge, this is the first work to jointly tackle conversion of singer
identity and vocal technique based on a deep learning approach.
