Generalized F0 modelling with absolute and relative pitch features for singing voice synthesis

Natural pitch fluctuations are essential to human singing. To effectively synthesize singing voice, the generation of these pitch fluctuations is necessary. Previous synthesis methods classify and reproduce them individually. These fluctuations, however, are found to be dependent and vary under different contexts. This paper proposes a generalized framework for F0 modelling to learn and generate these fluctuations on a note basis. Context-dependent hidden Markov models, representing the possible fluctuations observed in particular musical contexts, are built. To capture the pitch fluctuation and the voicing transitions in human singing, we employ both absolute and relative pitch as the modelling features. Results of our experiments on pitch accuracy and quality of synthesized singing showed that the proposed framework achieves accurate pitch generation and better naturalness of synthesized outputs.