no code implementations • 24 Jun 2020 • Toru Nakashika, Kohei Yatabe
Its conditional distribution of the observable data is given by the gamma distribution, and thus the proposed RBM can naturally handle the data represented by positive numbers as the amplitude spectra.
1 code implementation • 29 Oct 2018 • Shinji Takaki, Toru Nakashika, Xin Wang, Junichi Yamagishi
This paper proposes a new loss using short-time Fourier transform (STFT) spectra for the aim of training a high-performance neural speech waveform model that predicts raw continuous speech waveform samples directly.
no code implementations • 27 Mar 2018 • Toru Nakashika, Shinji Takaki, Junichi Yamagishi
In contrast, the proposed feature extractor using the CRBM directly encodes the complex spectra (or another complex-valued representation of the complex spectra) into binary-valued latent features (hidden units).
