Singing voice synthesizer with non-filter waveform generation using spectral phase reconstruction

2016 ◽  
Vol 140 (4) ◽  
pp. 2960-2961
Author(s):  
Yasuhiro Hamada ◽  
Nobutaka Ono ◽  
Shigeki Sagayama
Keyword(s):  
Spectral Phase ◽  
Singing Voice ◽  
Phase Reconstruction ◽  
Waveform Generation

scholarly journals Effects of noise on spectral phase reconstruction with wavelet analysis

2005 ◽  
Vol 54 (9) ◽  
pp. 4176
Author(s):  
Deng Yu-Qiang ◽  
Zhang Zhi-Gang ◽  
Chai Lu ◽  
Wang Qing-Yue
Keyword(s):  
Wavelet Analysis ◽  
Spectral Phase ◽  
Phase Reconstruction

scholarly journals Generalized spectral phase-only time-domain ptychographic phase reconstruction applied in nonlinear microscopy

2020 ◽  
Vol 37 (11) ◽  
pp. A285
Author(s):  
George Dwapanyin ◽  
Dirk Spangenberg ◽  
Alexander Heidt ◽  
Thomas Feurer ◽  
Gurthwin Bosman ◽  
...  
Keyword(s):  
Time Domain ◽  
Spectral Phase ◽  
Nonlinear Microscopy ◽  
Phase Reconstruction

Recognizing emotions in the singing voice.

2017 ◽  
Vol 27 (4) ◽  
pp. 244-255 ◽  
Author(s):  
Klaus R. Scherer ◽  
Stéphanie Trznadel ◽  
Bernardino Fantini ◽  
Johan Sundberg
Keyword(s):  
Singing Voice

An Absolute Phase Estimation Method for Interferometry Imagery

2019 ◽  
Vol 1 (2) ◽  
pp. 14-19
Author(s):  
Sui Ping Lee ◽  
Yee Kit Chan ◽  
Tien Sze Lim
Keyword(s):  
Noise Suppression ◽  
Estimation Method ◽  
Phase Image ◽  
Phase Estimation ◽  
Phase Reconstruction ◽  
Estimation Algorithms ◽  
Absolute Phase ◽  
Filtering Rate ◽  
Interferometric Phase ◽  
Image Performance

Accurate interpretation of interferometric image requires an extremely challenging task based on actual phase reconstruction for incomplete noise observation. In spite of the establishment of comprehensive solutions, until now, a guaranteed means of solution method is yet to exist. The initially observed interferometric image is formed by 2π-periodic phase image that wrapped within (-π, π]. Such inverse problem is further corrupted by noise distortion and leads to the degradation of interferometric image. In order to overcome this, an effective algorithm that enables noise suppression and absolute phase reconstruction of interferometric phase image is proposed. The proposed method incorporates an improved order statistical filter that is able to adjust or vary on its filtering rate by adapting to phase noise level of relevant interferometric image. Performance of proposed method is evaluated and compared with other existing phase estimation algorithms. The comparison is based on a series of computer simulated and real interferometric data images. The experiment results illustrate the effectiveness and competency of the proposed method.

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