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检索条件"主题词=Variational autoencoder"

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Single Cell Multi-Modal Analysis Using scDMVAE with an Emphasis on SCoPE2 Technology

Single Cell Multi-Modal Analysis Using scDMVAE with an Empha...

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作者： Zheng, Yi University of California Santa Barbara

学位级别：Ph.D., Doctor of Philosophy

Effective multi-modal integration of single cell datasets is critical for uncovering the biological properties of cells from different molecular perspectives. However, this poses significant challenges, including how to preserve shared information and account for differences between differently distributed datasets, how to integrate datasets linked by different anchors (cells or features) and how to improve the quality of datasets for integration. In this dissertation, we introduce two novel models that address these challenges. First, we present scDMVAE, a neural network model that can capture both shared and data-specific aspects of datasets in a latent space. scDMVAE can handle both cell-linked and feature-linked datasets through its embedding learning and attention-based matching components, respectively. We demonstrate the effectiveness of scDMVAE on a cell-linked CITE-seq dataset to reveal different cell type relations between mRNA and protein, and on feature-linked SCoPE2 proteomics and scRNA-Seq mRNA human testis datasets to transfer labels from mRNA to protein. Additionally, we present PCRID, a principal curve based model that aligns the retention time of peptides to improve confidence estimates of peptide-spectrum-matches (PSMs) in SCoPE2 technology. PCRID outperforms existing models like DART-ID by handling non-linearities in retention time more effectively, increasing the identification rate of peptides by 154.53 % at a PEP threshold of 0.01 while controlling false discoveries. Together, these models represent significant advances in single cell data analysis and have broad applications across related fields.

关键词： Multi-modal data integration variational autoencoder Single-cell data integration Proteomics data CITE-seq dataset

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A Preliminary Study of a Two-Stage Paradigm for Preserving Speaker Identity in Dysarthric Voice Conversion 22

A Preliminary Study of a Two-Stage Paradigm for Preserving S...

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Interspeech Conference

作者： Huang, Wen-Chin Kobayashi, Kazuhiro Peng, Yu-Huai Liu, Ching-Feng Tsao, Yu Wang, Hsin-Min Toda, Tomoki Nagoya Univ Nagoya Aichi Japan Acad Sinica Taipei Taiwan Chi Mei Hosp Tainan Taiwan

ISBN: (纸本)9781713836902

We propose a new paradigm for maintaining speaker identity in dysarthric voice conversion (DVC). The poor quality of dysarthric speech can be greatly improved by statistical VC, but as the normal speech utterances of a dysarthria patient are nearly impossible to collect, previous work failed to recover the individuality of the patient. In light of this, we suggest a novel, two-stage approach for DVC, which is highly flexible in that no normal speech of the patient is required. First, a powerful parallel sequence-to-sequence model converts the input dysarthric speech into a normal speech of a reference speaker as an intermediate product, and a nonparallel, frame-wise VC model realized with a variational autoencoder then converts the speaker identity of the reference speech back to that of the patient while assumed to be capable of preserving the enhanced quality. We investigate several design options. Experimental evaluation results demonstrate the potential of our approach to improving the quality of the dysarthric speech while maintaining the speaker identity.

关键词： dysarthric voice conversion sequence-to-sequence modeling nonparallel voice conversion variational autoencoder

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Active Patterns Perceived for Stochastic Video Prediction 22

Active Patterns Perceived for Stochastic Video Prediction

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30th ACM International Conference on Multimedia (MM)

作者： Xu, Yechao Sun, Zhengxing Li, Qian Sun, Yunhan Luo, Shoutong Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China Natl Univ Def Technol Coll Meteorol & Oceanog Changsha Hunan Peoples R China

ISBN: (纸本)9781450392037

Predicting future scenes based on historical frames is challenging, especially when it comes to the complex uncertainty in nature. We observe that there is a divergence between spatial-temporal variations of active patterns and non-active patterns in a video, where these patterns constitute visual content and the former ones implicate more violent movement. This divergence enables active patterns the higher potential to act with more severe future uncertainty. Meanwhile, the existence of non-active patterns provides an opportunity for machines to examine some underlying rules with a mutual constraint between non-active patterns and active patterns. In order to solve this divergence, we provide a method called active patterns-perceived stochastic video prediction (ASVP) which allows active patterns to be perceived by neural networks during training. Our method starts with separating active patterns along with non-active ones from a video. Then, both scene-based prediction and active pattern-perceived prediction are conducted to respectively capture the variations within the whole scene and active patterns. Specially for active pattern-perceived prediction, a conditional generative adversarial network (CGAN) is exploited to model active patterns as conditions, with a variational autoencoder (VAE) for predicting the complex dynamics of active patterns. Additionally, a mutual constraint is designed to improve the learning procedure for the network to better understand underlying interacting rules among these patterns. Extensive experiments are conducted on both KTH human action and BAIR action-free robot pushing datasets with comparison to state-of-the-art works. Experimental results demonstrate the competitive performance of the proposed method as we expected. The released code and models are at https://***/tolearnmuch/ASVP.

关键词： Video prediction Stochastic video prediction Conditional generative adversarial network variational autoencoder Active pattern mining

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A VAE Conversion Method for Private Data Linkage 26

A VAE Conversion Method for Private Data Linkage

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26th IEEE Pacific Rim International Symposium on Dependable Computing (PRDC)

作者： Tai, Bo-Chen Li, Szu-Chuang Huang, Yennun Acad Sinica Res Ctr Informat Technol Innovat Taipei Taiwan Tamkang Univ Dept Informat Commun New Taipei Taiwan

ISBN: (纸本)9781665424769

Data linkage plays a crucial role in realizing big data's value but is often regarded as a threat to personal privacy. Regulations like GDPR requires users' consent on each specific use of data, which is not practical for data analyzers. In this study, we propose a way to address the problem by having a trustworthy third party collect data from two or more parties, then use the data to train one or more variational autoencoder (VAE) models to remove privacy and send them to the data providers. Using this model, the users express their consent to share data with a trustworthy party. The third party links data from various datasets together to build a variational autoencoder model that allows all parties to generate datasets with full attributes without revealing sensitive personal data. System architectures and machine learning accuracy of generated data sets are measured in this study.

关键词： data linkage synthetic data variational autoencoder

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Rate Controllable Learned Image Compression Based on RFL Model

Rate Controllable Learned Image Compression Based on RFL Mod...

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IEEE International Conference on Visual Communications and Image Processing (VCIP)

作者： Zhang, Saiping Wang, Luge Mao, Xionghui Yang, Fuzheng Wan, Shuai Xidian Univ Sch Telecommun Engn Xian Peoples R China Northwestern Polytech Univ Sch Elect & Informat Xian Peoples R China

ISBN: (纸本)9781665475921

In this paper, we propose a rate controllable image compression framework, Rate Controllable variational autoencoder (RC-VAE), based on the Rate-Feature-Level (RFL) model established through our exploration on the correlation among target rates, image features and quantization levels. Considering that, when meeting the same target rate, different images should be quantized in different levels, we focus on jointly utilizing the target rate and the extracted features of the image to predict the corresponding quantization level and propose the RFL model. Combining the proposed RFL model with a Hyperprior Continuously Variable Rate (HCVR) image compression network, we further propose the RC-VAE. By controlling information loss in quantization process, the RC-VAE can work at the target rate. Experimental results have demonstrated that one single RC-VAE model can adapt to multiple target rates with higher rate control accuracy and better R-D performance compared with the stateof-the-art rate controllable image compression networks.

关键词： Deep image compression rate control variational autoencoder rate-distortion

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Audio-Visual Speech Enhancement with a Deep Kalman Filter Generative Model 48

Audio-Visual Speech Enhancement with a Deep Kalman Filter Ge...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Golmakani, Ali Sadeghi, Mostafa Serizel, Romain Université de Lorraine Cnrs Inria Loria NancyF-54000 France

ISBN: (纸本)9781728163277

Deep latent variable generative models based on variational autoencoder (VAE) have shown promising performance for audio-visual speech enhancement (AVSE). The underlying idea is to learn a VAE-based audio-visual prior distribution for clean speech data, and then combine it with a statistical noise model to recover a speech signal from a noisy audio recording and video (lip images) of the target speaker. Existing generative models developed for AVSE do not take into account the sequential nature of speech data, which prevents them from fully incorporating the power of visual data. In this paper, we present an audio-visual deep Kalman filter (AV-DKF) generative model which assumes a first-order Markov chain model for the latent variables and effectively fuses audio-visual data. Moreover, we develop an efficient inference methodology to estimate speech signals at test time. We conduct a set of experiments to compare different variants of generative models for speech enhancement. The results demonstrate the superiority of the AV-DKF model compared with both its audio-only version and the non-sequential audio-only and audio-visual VAE-based models. © 2023 IEEE.

关键词： Audio-visual speech enhancement deep Kalman filter generative model variational autoencoder

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dB: A Web-based Drummer Bot for Finger-Tapping

dB: A Web-based Drummer Bot for Finger-Tapping

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International Conference on New Interfaces for Musical Expression, NIME 2024

作者： Erdem, Çağrı Griwodz, Carsten Department of Informatics University of Oslo Oslo Norway

dB is a web-based interface that serves as a "drummer bot" for exploring interactive groove-making experiences with an AI percussion system. This system, leveraging variational autoencoders (VAEs), transforms simple rhythmic inputs into complex drum patterns with microtiming and dynamics. Designed for accessibility and playfulness, dB is easily operated via a computer keyboard, making it suitable for a wide range of users. This paper outlines dB’s foundational concepts, data collection, and a comprehensive overview of system and interface architecture. We then present our preliminary user study that investigated specific aspects of user engagement, including joy and boredom states, as well as perceptions of effort and control. The study’s results underscore the musical background, expertise, and generational differences as significant influences on user experiences. Notably, test conditions characterized by greater randomness and rhythmic variation were consistently perceived as more engaging, and emerging trends were observed in user responses diverging over time. © 2024, International Conference on New Interfaces for Musical Expression. All rights reserved.

关键词： generative models Human-AI collaboration rhythm pattern generation user studies variational autoencoder

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DiffVoice: Text-to-Speech with Latent Diffusion 48

DiffVoice: Text-to-Speech with Latent Diffusion

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Liu, Zhijun Guo, Yiwei Yu, Kai Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-Lance Lab Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9781728163277

In this work, we present DiffVoice, a novel text-to-speech model based on latent diffusion. We propose to first encode speech signals into a phoneme-rate latent representation with a variational autoencoder enhanced by adversarial training, and then jointly model the duration and the latent representation with a diffusion model. Subjective evaluations on LJSpeech and LibriTTS datasets demonstrate that our method beats the best publicly available systems in naturalness. By adopting recent generative inverse problem solving algorithms for diffusion models, DiffVoice achieves the state-of-the-art performance in text-based speech editing, and zero-shot adaptation. © 2023 IEEE.

关键词： diffusion probabilistic model speech editing speech synthesis variational autoencoder zero-shot adaptation

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Two-stage instrument timbre transfer method using RAVE 26

Two-stage instrument timbre transfer method using RAVE

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26th International Symposium on Multimedia, ISM 2024

作者： Hu, Di Ito, Katunobu Hosei University Graduate School of Computer and Information Sciences Tokyo Japan Hosei University Faculty of Computer and Information Sciences Tokyo Japan

ISBN: (纸本)9798331511111

Recently, the real-time audio variational autoencoder (RAVE) method was developed for high-quality audio waveform synthesis. The RAVE method is based on a variational autoencoder and employs a two-stage training strategy. However, the RAVE model still has limitations in timbre transformation, especially when converting between instruments with significantly different timbres. Issues such as pitch instability, inaccurate timbre reproduction, and severe degradation in sound quality can arise. To enhance timbre transfer performance, we propose a two-stage timbre transformation method using RAVE, which involves applying two timbre transfer models to perform a dual transformation on the original input audio. To evaluate the proposed method, we trained the model and tested its performance using audio generated from MIDI and SoundFont2 sound sources. The results demonstrate that the proposed method improves timbre transfer compared to the single-stage RAVE model. © 2024 IEEE.

关键词： audio synthesis timbre transfer variational autoencoder

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Data-driven fault diagnosis based on the integrated deep nonlinear dynamic system model 43

Data-driven fault diagnosis based on the integrated deep non...

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43rd Chinese Control Conference, CCC 2024

作者： Tang, Xiaochu Tao, Na Zhang, Yi Li, Yuan Shenyang Aerospace University School of Automation Shenyang China Shenyang University of Chemical Technology College of Information Engineering Shenyang China

ISBN: (纸本)9789887581581

To ensure the safety and reliability of complex industrial processes are very important. Therefore, extracting multiple features of data effectively is a great significance to improve the accuracy of modeling for fault diagnosis. Dynamic, uncertainty and nonlinearity are main characteristics of industrial process data. However, it is challenging for modeling to extract multiple features of process data at the same time. In this paper, an integrated nonlinear dynamic system model is proposed for fault diagnosis based on variational autoencoder-linear dynamic system (VAE-LDS). First, the deep learning algorithm variational autoencoder (VAE) is used to extract the nonlinear data feature and learn the potential representation of data. Furthermore, the VAE model is embedded into the linear dynamic system (LDS) so that the dynamics and uncertainty underlying data can be extracted simultaneously. In this way, A comprehensive model integrating multiple features can be established. Finally, the proposed method is applied to the TE process for fault diagnosis comparing with other methods. The results show the proposed method has superior performance. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Data-driven Fault Detection Fault Diagnosis Linear Dynamic System variational autoencoder

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