5 accepted papers
The class of disentangled sequential auto-encoders factorises speech into time-invariant (global) and time-variant (local) representations for speaker identity and linguistic content, respectively. Many of the existing models employ this assumption to tackle zero-shot voice conversion (VC), which co…
Disentangled representation learning seeks to align individual dimensions or separate groups of coordinates of latent factors with attributes of observed data such that perturbing certain latent factors uniquely changes particular attributes. A main challenge in unsupervised disentanglement using au…
Disentangled sequential autoencoders (DSAEs) represent a class of probabilistic graphical models that describes an observed sequence with dynamic latent variables and a static latent variable. The former encode information at a frame rate identical to the observation, while the latter globally gover…
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…
Expressive singing voice correction is an appealing but challenging problem. A robust time-warping algorithm which synchronizes two singing recordings can provide a promising solution. We thereby propose to address the problem by canonical time warping (CTW) which aligns amateur singing recordings t…