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Speech Source Separation Using variational autoencoder and Bandpass Filter

IEEE ACCESS 2020年 8卷 156219-156231页

作者： Do, Hao Duc Tran, Son Thai Chau, Duc Thanh Univ Sci Fac Informat Technol Ho Chi Minh City 70000 Vietnam Viet Nam Natl Univ Ho Chi Minh City 70000 Vietnam Univ Sci Off Educ & Training Ho Chi Minh City 70000 Vietnam OLLI Technol JSC AI Lab Ho Chi Minh City 70000 Vietnam

Speech source separation is essential for speech-related applications because this process enhances the input speech signal for the main processing model. Most of the current approaches for this task focus on separating the speech of commonly high-frequency noises or a particular background sound. They cannot clear the signals which intersect with the human speech in its frequency range. To deal with this problem, we propose a hybrid approach combining a variational autoencoder (VAE) and a bandpass filter (BPF). This method can extract and enhance the speech signal in the mixture of many elements such as speech signal, the high-frequency noises, and many kinds of different background sounds which interfere with the speech sound. Experimental results showed that our model can extract effectively the speech signal with 15.02 dB in Signal to Interference Ratio (SIR) and 12.99 dB in Signal to Distortion Ratio (SDR). On the other hand, we can adjust the passband to identify the range of frequency at the output signal to apply for a particular application like gender classification.

关键词： Speech enhancement Blind source separation Band-pass filters Music Multiple signal classification Generative model variational autoencoder bandpass filter speech separation

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A deep adversarial variational autoencoder model for dimensionality reduction in single-cell RNA sequencing analysis

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BMC BIOINFORMATICS 2020年第1期21卷 64-64页

作者： Lin, Eugene Mukherjee, Sudipto Kannan, Sreeram Univ Washington Dept Elect & Comp Engn Seattle WA 98195 USA Univ Washington Dept Biostat Seattle WA 98195 USA China Med Univ Grad Inst Biomed Sci Taichung Taiwan

Background Single-cell RNA sequencing (scRNA-seq) is an emerging technology that can assess the function of an individual cell and cell-to-cell variability at the single cell level in an unbiased manner. Dimensionality reduction is an essential first step in downstream analysis of the scRNA-seq data. However, the scRNA-seq data are challenging for traditional methods due to their high dimensional measurements as well as an abundance of dropout events (that is, zero expression measurements). Results To overcome these difficulties, we propose DR-A (Dimensionality Reduction with Adversarial variational autoencoder), a data-driven approach to fulfill the task of dimensionality reduction. DR-A leverages a novel adversarial variational autoencoder-based framework, a variant of generative adversarial networks. DR-A is well-suited for unsupervised learning tasks for the scRNA-seq data, where labels for cell types are costly and often impossible to acquire. Compared with existing methods, DR-A is able to provide a more accurate low dimensional representation of the scRNA-seq data. We illustrate this by utilizing DR-A for clustering of scRNA-seq data. Conclusions Our results indicate that DR-A significantly enhances clustering performance over state-of-the-art methods.

关键词： Adversarial autoencoder variational autoencoder Dimensionality reduction Generative adversarial networks Single-cell RNA sequencing

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Dirichlet variational autoencoder

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PATTERN RECOGNITION 2020年 107卷 107514-107514页

作者： Joo, Weonyoung Lee, Wonsung Park, Sungrae Moon, Il-Chul Korea Adv Inst Sci & Technol Dept Ind & Syst Engn Daejeon 34141 South Korea SK Telecom AI Technol Unit Seoul 04539 South Korea NAVER Corp Clova AI Res Gyeonggi Do 13561 South Korea

This paper proposes Dirichlet variational autoencoder (DirVAE) using a Dirichlet prior. To infer the parameters of DirVAE, we utilize the stochastic gradient method by approximating the inverse cumulative distribution function of the Gamma distribution, which is a component of the Dirichlet distribution. This approximation on a new prior led an investigation on the component collapsing, and DirVAE revealed that the component collapsing originates from two problem sources: decoder weight collapsing and latent value collapsing. The experimental results show that 1) DirVAE generates the result with the best log-likelihood compared to the baselines;2) DirVAE produces more interpretable latent values with no collapsing issues which the baselines suffer from;3) the latent representation from DirVAE achieves the best classification accuracy in the (semi-)supervised classification tasks on MNIST, OMNIGLOT, COIL-20, SVHN, and CIFAR-10 compared to the baseline VAEs;and 4) the DirVAE augmented topic models show better performances in most cases. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Representation learning variational autoencoder Deep generative model Multi-modal latent representation Component collapse

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Bayesian finite element model updating with a variational autoencoder and polynomial chaos expansion

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ENGINEERING STRUCTURES 2024年 316卷

作者： Li, Qiang Ni, Pinghe Du, Xiuli Han, Qiang Xu, Kun Bai, Yulei Natl Key Lab Bridge Safety & Resilience Beijing Peoples R China

The quantification of uncertainty in civil structures poses a significant challenge in contemporary research due to the substantial computational demands involved. This study introduces an innovative approach for updating the finite element model (FEM) and quantifying uncertainties in civil structures through the synergistic use of variational autoencoder (VAE) and polynomial chaos expansion (PCE). Within this framework, the unknown parameters inherent to the structural FEM are represented as latent variables and can be effectively inferred through the VAE. These latent variables are modeled using a multivariate Gaussian distribution. In the proposed methodology, the PCE serves to approximate the log-likelihood function associated with the latent variables, facilitating the derivation of the analytic expression for the variational lower bound. By maximizing this variational lower bound, both the mean and standard deviation can be readily determined. To assess the accuracy and computational efficiency of the proposed technique, numerical analyses are performed on a cantilever beam and a steel pedestrian bridge. Furthermore, the effectiveness of the proposed approach is validated through its application to damage identification within a benchmark model. Significantly, the results indicate that the proposed method offers superior computational efficiency compared to the conventional VAE approach. Notably, the findings reveal that employing a high-order PCE is unnecessary;rather, a low-order PCE suffices for precise parameter identification. Consequently, the proposed methodology necessitates only a limited dataset for training to ascertain the PCE coefficients, thereby enhancing its practical applicability and efficiency.

关键词： Bayesian model updating variational autoencoder variational inference Uncertainty quantification Polynomial chaos expansion

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Multiscale variational autoencoder regressor for production prediction and energy saving of industrial processes

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CHEMICAL ENGINEERING SCIENCE 2024年 284卷

作者： Han, Yongming Wang, Yue Chen, Zhiwei Lu, Yi Hu, Xuan Chen, Liangchao Geng, Zhiqiang Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Minist Educ China Engn Res Ctr Intelligent PSE Beijing 100029 Peoples R China Beijing Univ Chem Technol High Tech Res Inst Beijing 100029 Peoples R China Beijing Jiaboyunshu Technol Co Ltd Beijing 100089 Peoples R China

As modern industries increase in scale and integration, the industrial process data show more complex dynamic time variability. To solve the dynamic time-varying problem of industrial process data, a novel multiscale variational autoencoder (MSVAE) based Regressor (REG) (MSVAE-REG) is proposed for production prediction and energy saving. The encoder of the MSVAE is constructed by multiscale convolutional neural network (MSCNN) to extract the multiscale temporal dynamic features, including the overall and local trend features in the Gaussian latent space. Then, the decoder of the MSVAE recovers the input data to enhance the dynamic adaptability of multiscale features. Moreover, the REG utilizes the gated recurrent unit (GRU) to build a dynamic relationship between multiscale temporal dynamic features and the predicted output. Finally, the proposed model is verified on the propylene and ethylene production datasets. Compared with the back propagation (BP) network, the extreme learning machine (ELM), the radial basis function (RBF), the convolutional neural network (CNN) and the variational autoencoder (VAE), the MSVAE-REG achieves state-of-the-art results, with the prediction accuracy respectively reaching about 95% and 96%, providing a new judgment basis for optimizing the production process and saving energy.

关键词： variational autoencoder Multiscale convolutional neural network Energy saving Production prediction Propylene industrial production Ethylene industrial production

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DistVAE: Distributed variational autoencoder for sequential recommendation

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KNOWLEDGE-BASED SYSTEMS 2023年第1期264卷

作者： Li, Li Xiahou, Jianbing Lin, Fan Su, Songzhi Xiamen Univ Sch Informat Xiamen Peoples R China Quanzhou Normal Univ Quanzhou Peoples R China

Recommender systems (RS) play a vital role in daily life due to their practical significance. As a branch of RS, the sequential recommendation has attracted much attention because of its effectiveness in helping people find items of interest. Recently, the generative methods based on variational autoencoder (VAE) have shown promising performance in modeling temporal dependencies among items in user sequences. These methods require collecting all users' raw data, which incurs high costs and privacy issues. Therefore, this paper makes efforts to implement a distributed sequential VAE method. However, the distributed version of VAE-based sequential recommendation faces the following challenges. Firstly, in a distributed setting, there is a small amount of data on a single device, increasing the randomness of the gradients used to update the global model. Meanwhile, users in recommender systems usually have significantly different preference patterns. It may magnify the above randomness and make the global model update unstable. Secondly, existing generative methods usually adopt GRU to model latent variable dependencies, but GRU performs poorly in modeling the association in long sequences. This limitation makes these methods fail to model the long-term dependence in the inference model. To solve the problems, this paper proposes Distributed variational autoencoder for the sequential recommendation (DistVAE), where a trusted server coordinates thousands of clients to train an insightful generative model without pulling their raw data together. Specifically, the clustering algorithm is integrated into the distributed sequential recommendation framework to dynamically find peers with similar preferences, and a sequential VAE is adopted to wrap a masked attention layer to model the long-term dependence. Experiments on four real-world datasets show that DistVAE outperforms other state-of-the-art centralized methods under the distributed training framework. (c) 2023 Elsevie

关键词： Sequential recommendation variational autoencoder Distributed learning

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Advanced collision risk estimation in terminal manoeuvring areas using a variational autoencoder for

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartB期133卷

作者： Krauth, Timothe Morio, Jerome Olive, Xavier Figuet, Benoit Zurich Univ Appl Sci Ctr Aviat CH-8400 Winterthur Switzerland Univ Toulouse ONERA DTIS F-31055 Toulouse France Technikumstr 71 CH-8400 Winterthur Switzerland

Air Traffic Management aims at ensuring safety during aircraft operations, particularly within Terminal Manoeuvring Areas where traffic density is high. The challenge lies in balancing safety and efficiency by closely managing the likelihood of mid-air collisions regarding the airport movements. Traditional models like the Reich and Anderson -Hsu models have been influential, but they fall short in representing the complex reality of Terminal Manoeuvring Areas. Data -driven approaches are emerging, with Monte Carlo simulations offering a more flexible methodology for collision risk estimation. This paper introduces a framework for assessing Mid -Air Collision likelihood resulting from Terminal Manoeuvring Area procedures by combining the field of Deep Generative Modelling using a variational autoencoder with the domain of rare event statistics through Subset Simulation. By incorporating disentanglement into the variational autoencoders model, we create a latent space that aligns dimensions with distinctive trajectory traits. Then, Subset Simulation is employed to gauge Mid -Air Collision probability, utilizing latent representations as input. Finally, sensitivity analysis reveals pivotal factors influencing collision risk, correlated with trajectory attributes via disentanglement. The methodology is applied to traffic around Zurich Airport: it evaluates the risk arising from go -around and take -off procedures using Automatic Dependent Surveillance -Broadcast data.

关键词： Air traffic management Rare event probability estimation Deep generative models variational autoencoder Uncertainty quantification

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Unleashing the Power of Hierarchical variational autoencoder for Predicting Breast Cancer

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IEEE ACCESS 2024年 12卷 195658-195670页

作者： Sreelekshmi, V. Pavithran, K. Nair, Jyothisha J. Amrita Vishwa Vidyapeetham Amrita Sch Comp Dept Comp Sci & Engn Amritapuri 690525 India Amrita Inst Med Sci Dept Med Oncol Kochi 682041 Kerala India

Breast cancer continues to be a major health concern worldwide. Early and accurate prediction is crucial for effective treatment and improving survival rates. Computer Aided Diagnosis system serves as an invaluable tool for radiologists, aiming to reduce diagnostic errors and enhance the accuracy of diagnosis. These systems incorporate various processing techniques, including pre-processing, segmentation, feature extraction, and classification. Moreover, deep learning methods frequently suffer from sub optimal performance and demand substantial computational resources. This study focuses on developing an automated classification model for mammography images to aid in breast cancer diagnosis. Our proposed model initiates with noise removal using median filters, followed by the removal of the pectoral muscle in images through the Canny-edge detection method. On these preprocessed images, we applied data augmentation using a two-point crossover technique, addressing issues of small datasets and class imbalances common in medical image analysis. The images then undergo multi-scale representation via the fourth-order complex diffusion algorithm. Feature extraction is conducted on these multi-scaled images using a Hierarchical variational Auto-encoders and then classified using a Support Vector Machine. Employing fourth-order complex diffusion for initial multi-scale representation significantly enhances the accuracy of feature extraction resulting in robust classification performance. The training process involves two different datasets like MIAS and the KAU-BCMD. Test results for the KAU-BCMD dataset include: accuracy of 99.80%, Area Under the Curve of 99.30%, F1-score of 99.20%, balanced accuracy of 99.80%, and Matthews correlation coefficient of 99.20%. For the MIAS dataset, test results show accuracy of 99.30%, Area Under the Curve of 99.10%, F1-score of 98.30%, balanced accuracy of 99.00%, and Matthews correlation coefficient of 99.00%. Our validation results clearl

关键词： Feature extraction Breast cancer Muscles Accuracy Data augmentation Mammography Image segmentation Noise Convolutional neural networks Classification algorithms convolutional neural network data augmentation edge detection multi-scale representation variational autoencoder

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Learning latent representations of bank customers with the variational autoencoder

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 164卷 114020-114020页

作者： Mancisidor, Rogelio A. Kampffmeyer, Michael Aas, Kjersti Jenssen, Robert UiT Arctic Univ Norway Fac Sci & Technol Dept Phys & Technol Hansine Hansens Veg 18 N-9037 Tromso Norway Santander Consumer Bank Credit Risk Models Strandveien 18 N-1325 Lysaker Norway Norwegian Comp Ctr POB 114 Blindern Oslo Norway UiT Machine Learning Grp Tromso Norway

Learning data representations that reflect the customers' creditworthiness can improve marketing campaigns, customer relationship management, data and process management or the credit risk assessment in retail banks. In this research, we show that it is possible to steer data representations in the latent space of the variational autoencoder (VAE) using a semi-supervised learning framework and a specific grouping of the input data called Weight of Evidence (WoE). Our proposed method learns a latent representation of the data showing a well-defied clustering structure. The clustering structure captures the customers' creditworthiness, which is unknown a priori and cannot be identified in the input space. The main advantages of our proposed method are that it captures the natural clustering of the data, suggests the number of clusters, captures the spatial coherence of customers' creditworthiness, generates data representations of unseen customers and assign them to one of the existing clusters. Our empirical results, based on real data sets reflecting different market and economic conditions, show that none of the well-known data representation models in the benchmark analysis are able to obtain well-defined clustering structures like our proposed method. Further, we show how banks can use our proposed methodology to improve marketing campaigns and credit risk assessment.

关键词： variational autoencoder Data representations Clustering Machine learning

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A topic modeling and image classification framework: The Generalized Dirichlet variational autoencoder

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PATTERN RECOGNITION 2024年 146卷

作者： Ojo, Akinlolu Oluwabusayo Bouguila, Nizar Concordia Univ 1455 Maisonneuve Blvd W Montreal PQ H3G 1M8 Canada

Latent Dirichlet allocation model (LDA) has been widely used in topic modeling. Recent works have shown the effectiveness of integrating neural network mechanisms with this generative model for learning text representation. However, one of the significant setbacks of LDA is that it is based on a Dirichlet prior that has a restrictive covariance structure. All its variables are considered to be negatively correlated, which makes the model restrictive. In a practical sense, topics can be positively or negatively correlated. To address this problem, we proposed a generalized Dirichlet variational autoencoder (GD-VAE) for topic modeling. The Generalized Dirichlet (GD) distribution has a more general covariance structure than the Dirichlet distribution because it takes into account both positively and negatively correlated topics in the corpus. Our proposed model leverages rejection sampling variational inference using a reparameterization trick for effective training. GDVAE compares favorably to recent works on topic models on several benchmark corpora. Experiments show that accounting for topics' positive and negative correlations results in better performance. We further validate the superiority of our proposed framework on two image data sets. GD-VAE demonstrates its significance as an integral part of a classification architecture. For reproducibility and further research purposes, code for this work can be found at https://***/hormone03/GD-VAE.

关键词： Generalized Dirichlet distribution Correlation variational autoencoder Topic models Reparameterization Image classification

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