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Found 31 papers, 14 papers with code
Generative Attribute Controller With Conditional Filtered Generative Adversarial Networks
This controller is based on a novel generative model called the conditional filtered generative adversarial network (CFGAN), which is an extension of the conventional conditional GAN (CGAN) that incorporates a filtering architecture into the generator input.
Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks
A subjective evaluation showed that the quality of the converted speech was comparable to that obtained with a Gaussian mixture model-based method under advantageous conditions with parallel and twice the amount of data.
Generative adversarial network-based approach to signal reconstruction from magnitude spectrograms
In this paper, we address the problem of reconstructing a time-domain signal (or a phase spectrogram) solely from a magnitude spectrogram.
Generative Adversarial Image Synthesis with Decision Tree Latent Controller
This paper proposes the decision tree latent controller generative adversarial network (DTLC-GAN), an extension of a GAN that can learn hierarchically interpretable representations without relying on detailed supervision.
StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks
This paper proposes a method that allows non-parallel many-to-many voice conversion (VC) by using a variant of a generative adversarial network (GAN) called StarGAN.
ACVAE-VC: Non-parallel many-to-many voice conversion with auxiliary classifier variational autoencoder
Such situations can be avoided by introducing an auxiliary classifier and training the encoder and decoder so that the attribute classes of the decoder outputs are correctly predicted by the classifier.
WaveCycleGAN: Synthetic-to-natural speech waveform conversion using cycle-consistent adversarial networks
The experimental results demonstrate that our proposed method can 1) alleviate the over-smoothing effect of the acoustic features despite the direct modification method used for the waveform and 2) greatly improve the naturalness of the generated speech sounds.
ConvS2S-VC: Fully convolutional sequence-to-sequence voice conversion
Second, it achieves many-to-many conversion by simultaneously learning mappings among multiple speakers using only a single model instead of separately learning mappings between each speaker pair using a different model.
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 (Seq2Seq) with attention and context preservation mechanism for voice conversion (VC) tasks.
Class-Distinct and Class-Mutual Image Generation with GANs
To overcome this limitation, we address a novel problem called class-distinct and class-mutual image generation, in which the goal is to construct a generator that can capture between-class relationships and generate an image selectively conditioned on the class specificity.
Label-Noise Robust Generative Adversarial Networks
To remedy this, we propose a novel family of GANs called label-noise robust GANs (rGANs), which, by incorporating a noise transition model, can learn a clean label conditional generative distribution even when training labels are noisy.
WaveCycleGAN2: Time-domain Neural Post-filter for Speech Waveform Generation
WaveCycleGAN has recently been proposed to bridge the gap between natural and synthesized speech waveforms in statistical parametric speech synthesis and provides fast inference with a moving average model rather than an autoregressive model and high-quality speech synthesis with the adversarial training.
Crossmodal Voice Conversion
We use the latent code of an input face image encoded by the face encoder as the auxiliary input into the speech converter and train the speech converter so that the original latent code can be recovered from the generated speech by the voice encoder.
CycleGAN-VC2: Improved CycleGAN-based Non-parallel Voice Conversion
Non-parallel voice conversion (VC) is a technique for learning the mapping from source to target speech without relying on parallel data.
Label-Noise Robust Multi-Domain Image-to-Image Translation
This problem is challenging in terms of scalability because it requires the learning of numerous mappings, the number of which increases proportional to the number of domains.
StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversion
To bridge this gap, we rethink conditional methods of StarGAN-VC, which are key components for achieving non-parallel multi-domain VC in a single model, and propose an improved variant called StarGAN-VC2.
Noise Robust Generative Adversarial Networks
Therefore, we propose distribution and transformation constraints that encourage the noise generator to capture only the noise-specific components.
Blur, Noise, and Compression Robust Generative Adversarial Networks
However, in contrast to NR-GAN, to address irreversible characteristics, we introduce masking architectures adjusting degradation strength values in a data-driven manner using bypasses before and after degradation.
Many-to-Many Voice Transformer Network
The main idea we propose is an extension of the original VTN that can simultaneously learn mappings among multiple speakers.
Nonparallel Voice Conversion with Augmented Classifier Star Generative Adversarial Networks
We previously proposed a method that allows for nonparallel voice conversion (VC) by using a variant of generative adversarial networks (GANs) called StarGAN.
CycleGAN-VC3: Examining and Improving CycleGAN-VCs for Mel-spectrogram Conversion
To address this, we examined the applicability of CycleGAN-VC/VC2 to mel-spectrogram conversion.
MaskCycleGAN-VC: Learning Non-parallel Voice Conversion with Filling in Frames
With FIF, we apply a temporal mask to the input mel-spectrogram and encourage the converter to fill in missing frames based on surrounding frames.
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 fundamental challenge in computer vision and graphics.
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-spectrogram is commonly applied as an intermediate representation, and the necessity for a mel-spectrogram vocoder is increasing.
AR-NeRF: Unsupervised Learning of Depth and Defocus Effects from Natural Images with Aperture Rendering Neural Radiance Fields
As an alternative to an AR-GAN, we propose an aperture rendering NeRF (AR-NeRF), which can utilize viewpoint and defocus cues in a unified manner by representing both factors in a common ray-tracing framework.
Unsupervised Intrinsic Image Decomposition with LiDAR Intensity
Intrinsic image decomposition (IID) is the task that decomposes a natural image into albedo and shade.
Wave-U-Net Discriminator: Fast and Lightweight Discriminator for Generative Adversarial Network-Based Speech Synthesis
This architecture provides a generator with sufficiently rich information for the synthesized speech to be closely matched to the real speech.
iSTFTNet2: Faster and More Lightweight iSTFT-Based Neural Vocoder Using 1D-2D CNN
Owing to the difficulty of a 1D CNN to model high-dimensional spectrograms, the frequency dimension is reduced via temporal upsampling.
MIMO-NeRF: Fast Neural Rendering with Multi-input Multi-output Neural Radiance Fields
We propose a multi-input multi-output NeRF (MIMO-NeRF) that reduces the number of MLPs running by replacing the SISO MLP with a MIMO MLP and conducting mappings in a group-wise manner.
Unsupervised Intrinsic Image Decomposition with LiDAR Intensity Enhanced Training
To address this challenge, we propose a novel approach that utilizes only an image during inference while utilizing an image and LiDAR intensity during training.
Training Generative Adversarial Network-Based Vocoder with Limited Data Using Augmentation-Conditional Discriminator
A generative adversarial network (GAN)-based vocoder trained with an adversarial discriminator is commonly used for speech synthesis because of its fast, lightweight, and high-quality characteristics.
