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We propose a flexible framework that deals with both singer conversion andsingers vocal technique conversion. The proposed model is trained onnon-parallel corpora, accommodates many-to-many conversion, and leveragesrecent advances of variational autoencoders. It employs separate encoders tolearn disentangled latent representations of singer identity and vocaltechnique separately, with a joint decoder for reconstruction. Conversion iscarried out by simple vector arithmetic in the learned latent spaces. Both aquantitative analysis as well as a visualization of the converted spectrogramsshow that our model is able to disentangle singer identity and vocal techniqueand successfully perform conversion of these attributes. To the best of ourknowledge, this is the first work to jointly tackle conversion of singeridentity and vocal technique based on a deep learning approach.

Singing Voice Conversion with Disentangled Representations of Singer and Vocal Technique Using Variational Autoencoders