Search Results for author: Toru Nakashika

Found 3 papers, 1 papers with code

Gamma Boltzmann Machine for Simultaneously Modeling Linear- and Log-amplitude Spectra

no code implementations24 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.

STFT spectral loss for training a neural speech waveform model

1 code implementation29 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.

Complex-Valued Restricted Boltzmann Machine for Direct Speech Parameterization from Complex Spectra

no code implementations27 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).


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