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检索条件"主题词=variational autoencoder"

共 1537 条记录，以下是1311-1320 订阅

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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...

引用

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

引用

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

引用

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

引用

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...

引用

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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An Ultrasound-Based Surveillance System for Bathroom Posture and Location Estimation

An Ultrasound-Based Surveillance System for Bathroom Posture...

引用

2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Sato, Shun Ohara, Ryotaro Kamarulzaman, M. Shahrul Amir Yasuda, Yuto Izumi, Shintaro Kawaguchi, Hiroshi Kobe University School of System Informatics Kobe Japan Kobe University School of Science Technology and Innovation Kobe Japan

ISBN: (纸本)9798350324136

Bathrooms can be slippery, increasing the risk of falling. In addition, because people enter the bathroom alone, it is difficult to detect accidents immediately when they occur. Therefore, a system is required to quickly detect accidents and call for assistance. In this study, the location and the posture of the bather are important attributes. We propose a method to estimate the location of a bather using ultrasound correlation values as input to a deep neural network and to estimate the bather's posture. Therefore, the mean absolute error achieved 11.7 cm for location estimation, and accuracy achieved 95.3% for posture estimation in the experiment involving an individual moving throughout the bathroom. © 2024 IEEE.

关键词： Imaging Location Estimation Posture Estimation Ultrasound variational autoencoder

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Adversarial Deep Neural Networks Effectively Remove Nonlinear Batch Effects from Gene-Expression Data

Adversarial Deep Neural Networks Effectively Remove Nonlinea...

引用

作者： Dayton, Jonathan Bryan Brigham Young University

学位级别：M.Sc., Master of Science

Gene-expression profiling enables researchers to quantify transcription levels in cells, thus providing insight into functional mechanisms of diseases and other biological processes. However, because of the high dimensionality of these data and the sensitivity of measuring equipment, expression data often contains unwanted confounding effects that can skew analysis. For example, collecting data in multiple runs causes nontrivial differences in the data (known as batch effects), known covariates that are not of interest to the study may have strong effects, and there may be large systemic effects when integrating multiple expression datasets. Additionally, many of these confounding effects represent higher-order interactions that may not be removable using existing techniques that identify linear patterns. We created Confounded to remove these effects from expression data. Confounded is an adversarial variational autoencoder that removes confounding effects while minimizing the amount of change to the input data. We tested the model on artificially constructed data and commonly used gene expression datasets and compared against other common batch adjustment algorithms. We also applied the model to remove cancer-type-specific signal from a pan-cancer expression dataset. Our software is publicly available at https://***/jdayton3/Confounded.

关键词： batch effects batch correction gene expression transcriptomics deep learning adversarial neural network variational autoencoder

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Training Reject-Classifiers for Out-of-distribution Detection via Explicit Boundary Sample Generation

Training Reject-Classifiers for Out-of-distribution Detectio...

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作者： Vernekar, Sachin University of Waterloo

学位级别：硕士

Discriminatively trained neural classifiers can be trusted only when the input data comes from the training distribution (in-distribution). Therefore, detecting out-of-distribution (OOD) samples is very important to avoid classification errors. In the context of OOD detection for image classification, one of the recent approaches proposes training a classifier called "confident-classifier" by minimizing the standard cross-entropy loss on in-distribution samples and minimizing the KL divergence between the predictive distribution of OOD samples in the low-density "boundary" of in-distribution and the uniform distribution (maximizing the entropy of the outputs). Thus, the samples could be detected as OOD if they have low confidence or high entropy. In this work, we analyze this setting both theoretically and experimentally. We also propose a novel algorithm to generate the "boundary" OOD samples to train a classifier with an explicit "reject" class for OOD samples. We show that this approach is effective in reducing high-confident miss-predictions on OOD samples while maintaining the test-error and high-confidence on the in-distribution samples compared to standard training. We compare our approach against several recent classifier-based OOD detectors including the confident-classifiers on MNIST and FashionMNIST datasets. Overall the proposed approach consistently performs better than others across most of the experiments.

关键词： Out-of-distribution detection Reject-Classifier variational autoencoder Manifold

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Learning Affordance Representations: An Efficient Learning Approach for End-to-End Visuomotor Control

Learning Affordance Representations: An Efficient Learning A...

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作者： Hämäläinen, Aleksi Aalto University

学位级别：硕士

The development of data-driven approaches, such as deep learning, has led to the emergence of systems that have achieved human-like performance in wide variety of tasks. For robotic tasks, deep data-driven models are introduced to create adaptive systems without the need of explicitly programming them. These adaptive systems are needed in situations, where task and environment changes remain unforeseen. Convolutional neural networks (CNNs) have become the standard way to process visual data in robotics. End-to-end neural network models that operate the entire control task can perform various complex tasks with little feature engineering. However, the adaptivity of these systems goes hand in hand with the level of variation in the training data. Training end-to-end deep robotic systems requires a lot of domain-, task-, and hardware-specific data, which is often costly to provide. In this work, we propose to tackle this issue by employing a deep neural network with a modular architecture, consisting of separate perception, policy, and trajectory parts. Each part of the system is trained fully on synthetic data or in simulation. The data is exchanged between parts of the system as low-dimensional representations of affordances and trajectories. The performance is then evaluated in a zero-shot transfer scenario using the Franka Panda robotic arm. Results demonstrate that a low-dimensional representation of scene affordances extracted from an RGB image is sufficient to successfully train manipulator policies.

关键词： robotics representation learning end-to-end visuomotor control variational autoencoder zero-shot transfer deep learning

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Integrating multi-tube flow cytometry data via deep generative modelling

Integrating multi-tube flow cytometry data via deep generati...

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作者： Hiltunen, Saara Aalto University

学位级别：硕士

Flow cytometry has been used for several decades to quantitatively analyse single cells in a high-throughput manner. This has resulted in a wide range of medical and biological applications. For example in immunology, flow cytometry data analysis identifies populations of immune cells based on cellular marker expression. As a consequence, flow cytometry has established itself as one of the main instruments in the diagnosis, monitoring and classification of leukemias, human immunodeficiency virus (HIV), and other diseases. State-of-the-art flow cytometers allow the detection of more than 20 cellular parameters, but the instruments used in clinical practice usually have much more limited capabilities. Thus, flow cytometry samples are often split into separate tubes with varying marker combinations to increase the number of measurable markers. However, this poses challenges to the analysis of flow cytometry data because the data from multiple tubes must be integrated while preserving the original biological information. Currently, most of the computational analysis techniques are not able to handle this kind of multi-tube flow cytometry data. In this work, we develop a deep generative modelling framework to enable simultaneous integration, clustering, and visualization of such data. We show that the model, named fcmVI, successfully discovers a latent representation of the cell types from flow cytometry data. Furthermore, we show that the fcmVI model can be used to align multiple tubes originating from the same sample in the latent space. The model is applied to two different data sets from mouse immune cells and human acute myeloid leukemia (AML) samples. In addition, the model enables the imputation of missing marker values for each tube, which is demonstrated on both data sets and the results are compared to nearest neighbor imputation.

关键词： flow cytometry neural networks generative modelling variational autoencoder adversarial learning

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