2 citations · 2 across the 1 of their papers we have counts for
3 papers
eess.AS2020★ 2 cited
Gamma Boltzmann Machine for Simultaneously Modeling Linear- and Log-amplitude Spectra
Toru Nakashika, Kohei Yatabe
In audio applications, one of the most important representations of audio signals is the amplitude spectrogram. It is utilized in many machine-learning-based information processing…
eess.AS2018
STFT spectral loss for training a neural speech waveform model
Shinji Takaki, Toru Nakashika, Xin Wang +1
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 continuou…
eess.AS2018
Complex-Valued Restricted Boltzmann Machine for Direct Speech Parameterization from Complex Spectra
Toru Nakashika, Shinji Takaki, Junichi Yamagishi
This paper describes a novel energy-based probabilistic distribution that represents complex-valued data and explains how to apply it to direct feature extraction from complex-valu…