[PDF] Top 20 Comparing Representations for Audio Synthesis Using Generative Adversarial Networks
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Comparing Representations for Audio Synthesis Using Generative Adversarial Networks
... Terms—audio, representations, synthesis, generative, adversarial ...learning for audio has shifted from using hand-crafted features requiring prior knowledge, to ... Voir le document complet
6
Speech synthesis using recurrent neural networks
... model for unconditional audio generation based on generating one audio sample at a ...neural networks in a hierarchical structure is able to capture underlying sources of variation in the ... Voir le document complet
74
Interactive Example-Based Terrain Authoring with Conditional Generative Adversarial Networks
... allow for fast terrain generation, they often fail to provide control over the terrain ...tedious for large ...terrains using small examples but provide low ...only for clas- si�cation but ... Voir le document complet
14
Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets
... image synthesis, video generation is still a more challenging task, due to the difficulty of generating the temporal motion of the ...GANs for video generation. For exam- ple, Wang et ... Voir le document complet
22
From attribute-labels to faces: face generation using a conditional generative adversarial network
... models for attribute-label based facial image and video generation incor- porating 2D and 3D deep conditional generative adversarial networks ...specific representations of generated ... Voir le document complet
7
Multi-view Generative Adversarial Networks
... task for a decade, training deep generative models still remains a ...properties for data generation (Bengio et ...name Generative Stochastic Networks by replacing the noise function C ... Voir le document complet
13
Generative Adversarial Networks for Realistic Synthesis of Hyperspectral Samples
... neural networks. Especially, we investigate generative adversarial networks and their applica- tion to the synthesis of consistent labeled ...such networks on public datasets, we ... Voir le document complet
5
Semantic Segmentation using Adversarial Networks
... of generative models that are able to synthesize realistic ...“deconvolutional” networks g(·) that progressively construct the image by up-sampling, using essentially a reverse CNN ...GANs for ... Voir le document complet
13
Audio Signal Representations for Factorization in the sparse domain
... used for factorization, important coding gains may be achieved with this ...of audio sig- nals that introduces robustness to time shifts, while increasing the sparsity of the representation, at a negligible ... Voir le document complet
5
Characterizing and comparing acoustic representations in convolutional neural networks and the human auditory system
... cut-off) for the two CompCor variants: temporal (tCompCor) and anatomical ...regions. For aCompCor, com- ponents are calculated within the intersection of the aforementioned mask and the union of CSF and WM ... Voir le document complet
177
Audio signal representations for indexing in the transform domain
... 1. Audio indexing on a very large database of coded ...These audio features were then combined with video features and used in a machine learning system in order to classify video ...low-level audio ... Voir le document complet
13
Generative adversarial networks as a novel approach for tectonic fault and fracture extraction in high resolution satellite and airborne optical images
... 1994 ; Mavrantza and Argialas , 2003 ; Aghaee Rad , 2019 ; Farah- bakhsh et al. , 2020 ), and are generally divided in two categories: unsupervised and supervised. The latter takes advantage of the expert knowledge and ... Voir le document complet
10
Simultaneous super-resolution and segmentation using a generative adversarial network: Application to neonatal brain MRI
... neural networks (CNNs) yields promising results for MRI data [2, ...5]. Generative adversarial networks (GANs) have thus been proposed to estimate textured and sharper images [5, ... Voir le document complet
5
Reconstructing faces from fMRI patterns using deep generative neural networks
... presented for 1 s, followed by an inter-stimulus interval of 2 s ...deep generative network. The training image set for each subject was drawn at random from the CelebA data set, with equal numbers ... Voir le document complet
11
Deep neural networks for audio scene recognition
... look for estimating the parameters. Recently, new procedures for training DNN ...lief Networks (DBN) and which are both probabilistic graphi- cal ...DNN for CASR problem by perform- ing ... Voir le document complet
6
Robust Articulatory Speech Synthesis using Deep Neural Networks for BCI Applications
... evaluation using behavioral testing Eleven subjects participated to an intelligibility ...following synthesis conditions were tested: analysis-synthesis, GMM based synthesis with and without ... Voir le document complet
6
FLAIR MR Image Synthesis By Using 3D Fully Convolutional Networks for Multiple Sclerosis
... Fig. 5: Evaluation result for lesion contrast. Our method keeps a good contrast between MS lesions and white matter. Even though MLP got a better contrast between lesions and the surrounding NAWM, the difference ... Voir le document complet
7
Comparing Signaling Networks between Normal and Transformed Hepatocytes Using Discrete Logical Models
... nM for IKK-2 and 400nm for IKK-1) and was originally identified by GlaxoSmithKline in a drug-discovery effort focused on rheumatoid arthritis and airway inflammation (13, ...target for the drug ... Voir le document complet
21
Impact of reverberation through deep neural networks on adversarial perturbations
... that using these pseudo-examples along with the new information during subsequent training benefit the preservation of the anterior ...of adversarial perturba- tions or detection in the broad sense ... Voir le document complet
10
An Adversarial Regularisation for Semi-Supervised Training of Structured Output Neural Networks
... method for semi-supervised training of structured-output neural ...of Generative Adversarial Networks (GAN), we train a discriminator to capture the notion of a ‘quality’ of network ...signal ... Voir le document complet
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