Takuhiro Kaneko

research

∙ 08/14/2023

iSTFTNet2: Faster and More Lightweight iSTFT-Based Neural Vocoder Using 1D-2D CNN

The inverse short-time Fourier transform network (iSTFTNet) has garnered...

0 Takuhiro Kaneko, et al. ∙

research

∙ 03/24/2023

Wave-U-Net Discriminator: Fast and Lightweight Discriminator for Generative Adversarial Network-Based Speech Synthesis

In speech synthesis, a generative adversarial network (GAN), training a ...

0 Takuhiro Kaneko, et al. ∙

research

∙ 03/20/2023

Unsupervised Intrinsic Image Decomposition with LiDAR Intensity

Intrinsic image decomposition (IID) is the task that decomposes a natura...

0 Shogo Sato, et al. ∙

research

∙ 06/13/2022

AR-NeRF: Unsupervised Learning of Depth and Defocus Effects from Natural Images with Aperture Rendering Neural Radiance Fields

Fully unsupervised 3D representation learning has gained attention owing...

0 Takuhiro Kaneko, et al. ∙

research

∙ 03/04/2022

iSTFTNet: Fast and Lightweight Mel-Spectrogram Vocoder Incorporating Inverse Short-Time Fourier Transform

In recent text-to-speech synthesis and voice conversion systems, a mel-s...

6 Takuhiro Kaneko, et al. ∙

research

∙ 06/24/2021

Unsupervised Learning of Depth and Depth-of-Field Effect from Natural Images with Aperture Rendering Generative Adversarial Networks

Understanding the 3D world from 2D projected natural images is a fundame...

10 Takuhiro Kaneko, et al. ∙

research

∙ 04/14/2021

FastS2S-VC: Streaming Non-Autoregressive Sequence-to-Sequence Voice Conversion

This paper proposes a non-autoregressive extension of our previously pro...

0 Hirokazu Kameoka, et al. ∙

research

∙ 02/25/2021

MaskCycleGAN-VC: Learning Non-parallel Voice Conversion with Filling in Frames

Non-parallel voice conversion (VC) is a technique for training voice con...

0 Takuhiro Kaneko, et al. ∙

research

∙ 10/22/2020

CycleGAN-VC3: Examining and Improving CycleGAN-VCs for Mel-spectrogram Conversion

Non-parallel voice conversion (VC) is a technique for learning mappings ...

0 Takuhiro Kaneko, et al. ∙

research

∙ 10/06/2020

VoiceGrad: Non-Parallel Any-to-Many Voice Conversion with Annealed Langevin Dynamics

In this paper, we propose a non-parallel any-to-many voice conversion (V...

0 Hirokazu Kameoka, et al. ∙

research

∙ 08/27/2020

Non-Parallel Voice Conversion with Augmented Classifier Star Generative Adversarial Networks

We have previously proposed a method that allows for non-parallel voice ...

3 Hirokazu Kameoka, et al. ∙

research

∙ 05/18/2020

Many-to-Many Voice Transformer Network

This paper proposes a voice conversion (VC) method based on a sequence-t...

6 Hirokazu Kameoka, et al. ∙

research

∙ 03/17/2020

Blur, Noise, and Compression Robust Generative Adversarial Networks

Recently, generative adversarial networks (GANs), which learn data distr...

7 Takuhiro Kaneko, et al. ∙

research

∙ 11/26/2019

Noise Robust Generative Adversarial Networks

Generative adversarial networks (GANs) are neural networks that learn da...

21 Takuhiro Kaneko, et al. ∙

research

∙ 07/29/2019

StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversion

Non-parallel multi-domain voice conversion (VC) is a technique for learn...

11 Takuhiro Kaneko, et al. ∙

research

∙ 05/06/2019

Label-Noise Robust Multi-Domain Image-to-Image Translation

Multi-domain image-to-image translation is a problem where the goal is t...

6 Takuhiro Kaneko, et al. ∙

research

∙ 04/09/2019

CycleGAN-VC2: Improved CycleGAN-based Non-parallel Voice Conversion

Non-parallel voice conversion (VC) is a technique for learning the mappi...

0 Takuhiro Kaneko, et al. ∙

research

∙ 04/09/2019

Crossmodal Voice Conversion

Humans are able to imagine a person's voice from the person's appearance...

0 Hirokazu Kameoka, et al. ∙

research

∙ 04/05/2019

WaveCycleGAN2: Time-domain Neural Post-filter for Speech Waveform Generation

WaveCycleGAN has recently been proposed to bridge the gap between natura...

0 Kou Tanaka, et al. ∙

research

∙ 11/27/2018

Label-Noise Robust Generative Adversarial Networks

Generative adversarial networks (GANs) are a framework that learns a gen...

14 Takuhiro Kaneko, et al. ∙

research

∙ 11/27/2018

Class-Distinct and Class-Mutual Image Generation with GANs

We describe a new problem called class-distinct and class-mutual (DM) im...

24 Takuhiro Kaneko, et al. ∙

research

∙ 11/09/2018

AttS2S-VC: Sequence-to-Sequence Voice Conversion with Attention and Context Preservation Mechanisms

This paper describes a method based on a sequence-to-sequence learning (...

0 Kou Tanaka, et al. ∙

research

∙ 11/05/2018

ConvS2S-VC: Fully convolutional sequence-to-sequence voice conversion

This paper proposes a voice conversion method based on fully convolution...

0 Hirokazu Kameoka, et al. ∙

research

∙ 09/25/2018

WaveCycleGAN: Synthetic-to-natural speech waveform conversion using cycle-consistent adversarial networks

We propose a learning-based filter that allows us to directly modify a s...

0 Kou Tanaka, et al. ∙

research

∙ 08/13/2018

ACVAE-VC: Non-parallel many-to-many voice conversion with auxiliary classifier variational autoencoder

This paper proposes a non-parallel many-to-many voice conversion (VC) me...

0 Hirokazu Kameoka, et al. ∙

research

∙ 06/06/2018

StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks

This paper proposes a method that allows for non-parallel many-to-many v...

2 Hirokazu Kameoka, et al. ∙

research

∙ 05/27/2018

Generative Adversarial Image Synthesis with Decision Tree Latent Controller

This paper proposes the decision tree latent controller generative adver...

0 Takuhiro Kaneko, et al. ∙

research

∙ 04/06/2018

Generative adversarial network-based approach to signal reconstruction from magnitude spectrograms

In this paper, we address the problem of reconstructing a time-domain si...

0 Keisuke Oyamada, et al. ∙

research

∙ 11/30/2017

Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks

We propose a parallel-data-free voice conversion (VC) method that can le...

0 Takuhiro Kaneko, et al. ∙

Takuhiro Kaneko

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