Previous [ 1] [ 2] [ 3] [ 4] [ 5] [ 6] [ 7] [ 8] [ 9] [ 10] [ 11] [ 12] [ 13] [ 14] [ 15]


Journal of Information Science and Engineering, Vol. 30 No. 2, pp. 425-442 (March 2014)

Singing-voice Synthesis Using ANN Vibrato-parameter Models

Department of Computer Science and Information Engineering
National Taiwan University of Science and Technology
Taipei, 106 Taiwan

Vibrato is an important factor that affects the naturalness level of a synthetic singing voice. Therefore, the analysis and modeling of vibrato parameters are studied in this paper. The vibrato parameters of those syllables segmented from recorded songs are analyzed by using short-time Fourier transform and the method of analytic signal. After the vibrato parameter values for all training syllables are extracted and normalized, they are used to train an artificial neural network (ANN) for each type of vibrato parameter. Then, these ANN models are used to generate the values of vibrato parameters. Next, these parameter values and other music information are used together to control a harmonic- plus-noise model (HNM) to synthesize Mandarin singing voice signals. With the synthetic singing voice, subjective perception tests are conducted. The results show that the singing voice synthesized with the ANN generated vibrato parameters is much increased in the naturalness level. Therefore, the combination of the ANN vibrato models and the HNM signal model is not only feasible for singing voice synthesis but also convenient to provide multiple singing voice timbres.

Keywords: singing voice, vibrato parameter, pitch contour, analytic signal, artificial neural network

Full Text () Retrieve PDF document (201403_09.pdf)

Received January 14, 2012; revised March 19 & May 1, 2012; accepted June 5, 2012.
Communicated by Hsin-Min Wang.
* The preliminary version has been presented in 2008 International Conference on Machine Learning and Cybernetics, July 12-15, 2008, Kunming, China and it was supported by National Science Council, Taiwan, under Grant No. NSC 96-2218-E-011-002.