Beijing Opera Synthesis Based on Straight Algorithm and Deep Learning - XueTing Wang; Cong Jin; Wei Zhao (Advances in Multimedia, 2018)
Speech synthesis is an important research content in the field of human-computer interaction and has a wide range of applications. As one of its branches, singing synthesis plays an important role. Beijing Opera is a famous traditional Chinese opera, and it is called Chinese quintessence. The singing of Beijing Opera carries some features of speech but it has its own unique pronunciation rules and rhythms which differ from ordinary speech and singing. In this paper, we propose three models for the synthesis of Beijing Opera. Firstly, the speech signals of the source speaker and the target speaker are extracted by using the straight algorithm. And then through the training of GMM, we complete the voice control model to input the voice to be converted and output the voice after the voice conversion. Finally, by modeling the fundamental frequency, duration, and frequency separately, a melodic control model is constructed using GAN to realize the synthesis of the Beijing Opera fragment. We connect the fragments and superimpose the background music to achieve the synthesis of Beijing Opera. The experimental results show that the synthesized Beijing Opera has some audibility and can basically complete the composition of Beijing Opera. We also extend our models to human-AI cooperative music generation: given a target voice of human, we can generate a Beijing Opera which is sung by a new target voice.