30 citations · 115 across the 34 of their papers we have counts for
7 papers · 1 filter
Online Phase Reconstruction via DNN-based Phase Differences Estimation
Yoshiki Masuyama, Kohei Yatabe, Kento Nagatomo +1
This paper presents a two-stage online phase reconstruction framework using causal deep neural networks (DNNs). Phase reconstruction is a task of recovering phase of the short-time…
WaveFit: An Iterative and Non-autoregressive Neural Vocoder based on Fixed-Point Iteration
Yuma Koizumi, Kohei Yatabe, Heiga Zen +1
Denoising diffusion probabilistic models (DDPMs) and generative adversarial networks (GANs) are popular generative models for neural vocoders. The DDPMs and GANs can be characteriz…
Measuring pitch extractors' response to frequency-modulated multi-component signals
Hideki Kawahara, Kohei Yatabe, Ken-Ichi Sakakibara +3
This article focuses on the research tool for investigating the fundamental frequencies of voiced sounds. We introduce an objective and informative measurement method of pitch extr…
An objective test tool for pitch extractors' response attributes
Hideki Kawahara, Kohei Yatabe, Ken-Ichi Sakakibara +3
We propose an objective measurement method for pitch extractors' responses to frequency-modulated signals. It enables us to evaluate different pitch extractors with unified criteri…
SpecGrad: Diffusion Probabilistic Model based Neural Vocoder with Adaptive Noise Spectral Shaping
Yuma Koizumi, Heiga Zen, Kohei Yatabe +2
Neural vocoder using denoising diffusion probabilistic model (DDPM) has been improved by adaptation of the diffusion noise distribution to given acoustic features. In this study, w…
Wearable SELD dataset: Dataset for sound event localization and detection using wearable devices around head
Kento Nagatomo, Masahiro Yasuda, Kohei Yatabe +2
Sound event localization and detection (SELD) is a combined task of identifying the sound event and its direction. Deep neural networks (DNNs) are utilized to associate them with t…
APPLADE: Adjustable Plug-and-play Audio Declipper Combining DNN with Sparse Optimization
Tomoro Tanaka, Kohei Yatabe, Masahiro Yasuda +1
In this paper, we propose an audio declipping method that takes advantages of both sparse optimization and deep learning. Since sparsity-based audio declipping methods have been de…