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检索条件"主题词=variational autoencoder"

共 1554 条记录，以下是471-480 订阅

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STATISTICAL SPEECH ENHANCEMENT BASED ON PROBABILISTIC INTEGRATION OF variational autoencoder AND NON-NEGATIVE MATRIX FACTORIZATION

STATISTICAL SPEECH ENHANCEMENT BASED ON PROBABILISTIC INTEGR...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Bando, Yoshiaki Mimura, Masato Itoyama, Katsutoshi Yoshii, Kazuyoshi Kawahara, Tatsuya Kyoto Univ Grad Sch Informat Sakyo Ku Kyoto 6068501 Japan RIKEN Ctr Adv Intelligence Project Chuo Ku Tokyo 1030027 Japan

ISBN: (纸本)9781538646588

This paper presents a statistical method of single-channel speech enhancement that uses a variational autoencoder (VAE) as a prior distribution on clean speech. A standard approach to speech enhancement is to train a deep neural network (DNN) to take noisy speech as input and output clean speech. Although this supervised approach requires a very large amount of pair data for training, it is not robust against unknown environments. Another approach is to use non-negative matrix factorization (NMF) based on basis spectra trained on clean speech in advance and those adapted to noise on the fly. This semi-supervised approach, however, causes considerable signal distortion in enhanced speech due to the unrealistic assumption that speech spectrograms are linear combinations of the basis spectra. Replacing the poor linear generative model of clean speech in NMF with a VAE - a powerful nonlinear deep generative model trained on clean speech, we formulate a unified probabilistic generative model of noisy speech. Given noisy speech as observed data, we can sample clean speech from its posterior distribution. The proposed method outperformed the conventional DNN-based method in unseen noisy environments.

关键词： Single-channel speech enhancement variational autoencoder Bayesian signal processing

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Laser variational autoencoder for map construction and self-localization

Laser variational autoencoder for map construction and self-...

引用

IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Wakita, Shohei Nakamura, Takayuki Hachiya, Hirotaka Wakayama Univ Fac Syst Engn 930 Sakaedani Wakayama 6408510 Japan

ISBN: (纸本)9781538666500

For accurate global self-localization with small memory usage, researches for the compression of the laser-scan data have been actively conducted. Main approaches to the compression are to design feature extractor based on human knowledge regarding the specific environment, e.g., office and hallway. However, in real robot navigation tasks such as a security patrol robot, the robot would be applied to a variety of environments and it is expensive if the users need to tune the design at every environment. To alleviate such problem, we propose to extend the state-of-the-art variational auto-encoder (VAE) by introducing the step-edge detector, which detects non-continuous transition emerged frequently at the laser scan data due to the limitation of distance measurement. With our proposed method, called "laserVAE". the feature extractor of the laser scan is automatically tuned given unknown environments. Through experiments with a real self-localization with 2D laser scan, we demonstrate the effectiveness of the proposed method.

关键词： Feature description of scan data variational autoencoder Scan matching Global self-localization

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Learning Community Structure with variational autoencoder 18

Learning Community Structure with Variational Autoencoder

引用

18th IEEE International Conference on Data Mining Workshops (ICDMW)

作者： Choong, Jun Jin Liu, Xin Murata, Tsuyoshi Tokyo Inst Technol Dept Comp Sci Tokyo Japan Natl Inst Adv Ind Sci & Technol Tokyo Japan

ISBN: (纸本)9781538691595

Discovering community structure in networks remains a fundamentally challenging task. From scientific domains such as biology, chemistry and physics to social networks the challenge of identifying community structures in different kinds of network is challenging since there is no universal definition of community structure. Furthermore, with the surge of social networks, content information has played a pivotal role in defining community structure, demanding techniques beyond its traditional approach. Recently, network representation learning have shown tremendous promise. Leveraging on recent advances in deep learning, one can exploit deep learning's superiority to a network problem. Most predominantly, successes in supervised and semi-supervised task has shown promising results in network representation learning tasks such as link prediction and graph classification. However, much has yet to be explored in the literature of community detection which is an unsupervised learning task. This paper proposes a deep generative model for community detection and network generation. Empowered with Bayesian deep learning, deep generative models are capable of exploiting non-linearities while giving insights in terms of uncertainty. Hence, this paper proposes variational Graph autoencoder for Community Detection (VGAECD). Extensive experiment shows that it is capable of outperforming existing state-of-the-art methods. The generalization of the proposed model also allows the model to be considered as a graph generator. Additionally, unlike traditional methods, the proposed model does not require a predefined community structure definition. Instead, it assumes the existence of latent similarity between nodes and allows the model to find these similarities through an automatic model selection process. Optionally, it is capable of exploiting feature-rich information of a network such as node content, further increasing its performance.

关键词： community detection variational autoencoder generative model

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Linked Causal variational autoencoder for Inferring Paired Spillover Effects 18

Linked Causal Variational Autoencoder for Inferring Paired S...

引用

27th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Rakesh, Vineeth Guo, Ruocheng Moraffah, Raha Agarwal, Nitin Liu, Huan Arizona State Univ Tempe AZ 85287 USA Univ Arkansas Little Rock AR 72204 USA

ISBN: (纸本)9781450360142

Modeling spillover effects from observational data is an important problem in economics, business, and other fields of research. It helps us infer the causality between two seemingly unrelated set of events. For example, if consumer spending in the United States declines, it has spillover effects on economies that depend on the U.S. as their largest export market. In this paper, we aim to infer the causation that results in spillover effects between pairs of entities (or units);we call this effect as paired spillover. To achieve this, we leverage the recent developments in variational inference and deep learning techniques to propose a generative model called Linked Causal variational autoencoder (LCVA). Similar to variational autoencoders (VAE), LCVA incorporates an encoder neural network to learn the latent attributes and a decoder network to reconstruct the inputs. However, unlike VAE, LCVA treats the latent attributes as confounders that are assumed to affect both the treatment and the outcome of units. Specifically, given a pair of units u and (u) over bar, their individual treatment and outcomes, the encoder network of LCVA samples the confounders by conditioning on the observed covariates of u, the treatments of both u and u and the outcome of u. Once inferred, the latent attributes (or confounders) of u captures the spillover effect of (u) over bar on u. Using a network of users from job training dataset (LaLonde (1986)) and co-purchase dataset from Amazon e-commerce domain, we show that LCVA is significantly more robust than existing methods in capturing spillover effects.

关键词： causal inference spillover effect variational autoencoder

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Facial Image Inpainting with variational autoencoder

Facial Image Inpainting with Variational Autoencoder

引用

IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE)

作者： Ching-Ting Tu Yi-Fu Chen Department of Applied Mathematics National Chung Hsing University Taichung City Taiwan Dept. of Computer Science and Information Engineering Tamkang University New Taipei City Taiwan

ISBN: (数字)9781728141923

ISBN: (纸本)9781728141930

This paper proposed a learning-based approach to reveal diversity possible appearances under the missing area of an occluded unseen image. In general, there are a lot of possible facial appearances for the missing area; for example, a male with a scarf, it is difficult to predict he has a beard in the covered area or not? In this paper, we propose a novel method for facial image inpainting, which generates the missing facial appearance by conditioning on the observable appearance. Given a trained standard variational autoencoder (VAE) for un-occluded face generation. To be specified, we search for the possible set of VAE coding vector for the current occluded input image, and the predicted coding should be robust to the missing area. The possible facial appearance set is then recovered through the decoder of VAE model. Experiments show that our method successfully predicts recovered results in large missing regions; these results are diverse, and all are reasonable to be consistent with the observable facial area, i.e., both the facial geometry and the personal characteristics are preserved.

关键词： image inpainting variational autoencoder sampling Face Sampling Imagery (Psychotherapy) Image images Facies Beard Scarves decoders occluding

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Optimizing variational Graph autoencoder for Community Detection

Optimizing Variational Graph Autoencoder for Community Detec...

引用

IEEE International Conference on Big Data (Big Data)

作者： Choong, Jun Jin Liu, Xin Murata, Tsuyoshi Tokyo Inst Technol Dept Comp Sci Tokyo Japan Natl Inst Adv Ind Sci & Technol Tokyo Japan

ISBN: (纸本)9781728108582

variational Graph autoencoders (VGAE) has recently been a popular framework of choice for learning representations on graphs. Its inception has allowed models to achieve state-of-the-art performances for challenging tasks such as link prediction, rating prediction and node clustering. However, a fundamental flaw exists in variational autoencoder (VAE) based approaches. Specifically, the objective function of VAE (reconstruction loss), deviates from its primary objective (i.e clustering). In this paper, we attempt to address this issue by introducing two significant changes to variational Graph autoencoder for Community Detection (VGAECD). Firstly, we introduce a simplified graph convolution encoder to increase convergence speed and reduce computational time. Secondly, a dual variational objective is introduced to encourage learning of the primary objective. The outcome is a faster converging model with competitive community detection performance.

关键词： community detection graph neural network variational autoencoder network embedding

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A Method to Inspect the Implementation of Electricity Price Based on Deep Learning variational autoencoder

A Method to Inspect the Implementation of Electricity Price ...

引用

IEEE Conference on Energy Internet and Energy System Integration

作者： Xiying Gao Ning Ye Jinchun Song Haomiao Wang Ye Zhang Yan Guan Xiaowen Song Yingkai Cai Wenshu Zhang Qian Hui Dan Li State Grid Liaoning Electric Power Research Institute Shenyang China The School of Mechanical Engineering and Automation Northeastern University Shenyang China State Grid Liaoning Electric Power Marketing Department Shenyang China State Grid Liaoyang Electric Power B&E Office Liaoyang China

In this paper, we propose a method for performing electricity price execution inspection by using a variational autoencoder technology in deep learning. The variational autoencoder based anomaly detection algorithm (VABAD) can be used both as a discriminant model and as a feature of the generation model, which effectively solves the calculation problem of multiple heterogeneous parameters of current electricity price inspection implementation. The reconstruction probability is a probabilistic measure that takes into account the variability of the distribution of variables. It is used by autoencoder based anomaly detection methods. Experimental results show that the proposed method has been validated and compared to the existing approaches. The databases used in this paper come from Power Marketing System that occurred in Liaoning, China in 2015.

关键词： Deep learning variational autoencoder Electricity price inspection implementation Data heterogeneity

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Statistical Speech Enhancement Based on Probabilistic Integration of variational autoencoder and Non-Negative Matrix Factorization

Statistical Speech Enhancement Based on Probabilistic Integr...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Yoshiaki Bando Masato Mimura Katsutoshi Itoyama Kazuyoshi Yoshii Tatsuya Kawahara Graduate School of Informatics Kyoto University Sakyo-ku Kyoto 606-8501 Japan

ISBN: (纸本)9781538646595

This paper presents a statistical method of single-channel speech enhancement that uses a variational autoencoder (VAE) as a prior distribution on clean speech. A standard approach to speech enhancement is to train a deep neural network (DNN) to take noisy speech as input and output clean speech. Although this supervised approach requires a very large amount of pair data for training, it is not robust against unknown environments. Another approach is to use non-negative matrix factorization (NMF) based on basis spectra trained on clean speech in advance and those adapted to noise on the fly. This semi-supervised approach, however, causes considerable signal distortion in enhanced speech due to the unrealistic assumption that speech spectrograms are linear combinations of the basis spectra. Replacing the poor linear generative model of clean speech in NMF with a VAE-a powerful nonlinear deep generative model-trained on clean speech, we formulate a unified probabilistic generative model of noisy speech. Given noisy speech as observed data, we can sample clean speech from its posterior distribution. The proposed method outperformed the conventional DNN-based method in unseen noisy environments.

关键词： Single-channel speech enhancement variational autoencoder Bayesian signal processing Speech Speech enhancement Noise matrix decomposition NMF protocol Unknown environments factorization Signal distortion

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UNSUPERVISED DOMAIN ADAPTATION FOR ROBUST SPEECH RECOGNITION VIA variational autoencoder-BASED DATA AUGMENTATION

UNSUPERVISED DOMAIN ADAPTATION FOR ROBUST SPEECH RECOGNITION...

引用

IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)

作者： Hsu, Wei-Ning Zhang, Yu Glass, James MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9781509047888

Domain mismatch between training and testing can lead to significant degradation in performance in many machine learning scenarios. Unfortunately, this is not a rare situation for automatic speech recognition deployments in real-world applications. Research on robust speech recognition can be regarded as trying to overcome this domain mismatch issue. In this paper, we address the unsupervised domain adaptation problem for robust speech recognition, where both source and target domain speech are available, but word transcripts are only available for the source domain speech. We present novel augmentation-based methods that transform speech in a way that does not change the transcripts. Specifically, we first train a variational autoencoder on both source and target domain data (without supervision) to learn a latent representation of speech. We then transform nuisance attributes of speech that are irrelevant to recognition by modifying the latent representations, in order to augment labeled training data with additional data whose distribution is more similar to the target domain. The proposed method is evaluated on the CHiME-4 dataset and reduces the absolute word error rate (WER) by as much as 35% compared to the non-adapted baseline.

关键词： unsupervised domain adaptation robust speech recognition variational autoencoder data augmentation

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Hidden Markov Model variational autoencoder for Acoustic Unit Discovery 18

Hidden Markov Model Variational Autoencoder for Acoustic Uni...

引用

18th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2017)

作者： Ebbers, Janek Heymann, Jahn Drude, Lukas Glarner, Thomas Haeb-Umbach, Reinhold Raj, Bhiksha Paderborn Univ Paderborn Germany Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781510848764

variational autoencoders (VAEs) have been shown to provide efficient neural-network-based approximate Bayesian inference for observation models for which exact inference is intractable. Its extension. the so-called Structured VAE (SVAE) allows inference in the presence of both discrete and continuous latent variables. Inspired by this extension, we developed a VAE with Hidden Markov Models (HMMs) as latent models. We applied the resulting HMM-VAE to the task of acoustic unit discovery in a zero resource scenario. Starting from an initial model based on variational inference in an HMM with Gaussian Mixture Model (GMM) emission probabilities, the accuracy of the acoustic unit discovery could be significantly improved by the HMM-VAE. In doing so we were able to demonstrate for an unsupervised learning task what is well-known in the supervised learning case: Neural networks provide superior modeling power compared to GMMs.

关键词： variational autoencoder hidden Markov Model unsupervised learning acoustic unit discovery

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