Suchergebnisse - multichannel autoencoder priors

Cauchy Multichannel Speech Enhancement with a Deep Speech Prior

Autoren: Fontaine, Mathieu Nugraha, Aditya Arie Badeau, Roland et al.

Weitere Verfasser: Fontaine, Mathieu Nugraha, Aditya Arie Badeau, Roland et al.

Quelle: 2019 27th European Signal Processing Conference (EUSIPCO). :1-5

Schlagwörter: Nonnegative matrix factorization, 03 medical and health sciences, multivariate complex Cauchy distribution, 0202 electrical engineering, electronic engineering, information engineering, Multichannel speech enhancement, variational autoencoder, 02 engineering and technology, 0305 other medical science, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing

Dateibeschreibung: application/pdf

Zugangs-URL: https://hal.telecom-paris.fr/hal-02288063/file/eusipco-2019-fontaine.pdf
https://telecom-paris.hal.science/hal-02288063v1
https://doi.org/10.23919/eusipco.2019.8903091
https://telecom-paris.hal.science/hal-02288063v1/document
https://dblp.uni-trier.de/db/conf/eusipco/eusipco2019.html#0002NBYL19

Fusing multichannel autoencoders with dynamic global loss for self-supervised fault diagnosis.

Autoren: Li, Chuan Xiong, Manjun Shen, Hongmeng et al.

Quelle: Computers in Industry. Jan2025, Vol. 164, pN.PAG-N.PAG. 1p.

Schlagwörter: *CONFLICT management, AUTOENCODERS, ROTATING machinery, PRIOR learning, SIGNALS & signaling

Multi-Channel and Multi-Model-Based Autoencoding Prior for Grayscale Image Restoration.

Autoren: Li, Sanqian Qin, Binjie Xiao, Jing et al.

Quelle: IEEE Transactions on Image Processing. 2020, Vol. 29, p142-156. 15p.

Schlagwörter: *IMAGE reconstruction, *MULTICHANNEL communication, *IMAGE denoising, *IMAGE processing, *NOISE control, *TASK analysis

Accelerated model-based iterative reconstruction strategy for sparse-view photoacoustic tomography aided by multi-channel autoencoder priors.

Autoren: Song X Zhong W Li Z et al.

Quelle: Journal of biophotonics [J Biophotonics] 2024 Jan; Vol. 17 (1), pp. e202300281. Date of Electronic Publication: 2023 Dec 07.

Publikationsart: Journal Article; Research Support, Non-U.S. Gov't

Info zur Zeitschrift: Publisher: Wiley-VCH Country of Publication: Germany NLM ID: 101318567 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1864-0648 (Electronic) Linking ISSN: 1864063X NLM ISO Abbreviation: J Biophotonics Subsets: MEDLINE

MeSH-Schlagworte: Image Processing, Computer-Assisted*/methods , Tomography, X-Ray Computed*/methods, Phantoms, Imaging ; Computer Simulation ; Algorithms

Compressive Reconstruction Based on Sparse Autoencoder Network Prior for Single-Pixel Imaging.

Autoren: Zeng, Hong Dong, Jiawei Li, Qianxi et al.

Quelle: Photonics; Oct2023, Vol. 10 Issue 10, p1109, 21p

Schlagwörter: PIXELS, MULTICHANNEL communication, COMPRESSED sensing, FEATURE extraction, IMAGE reconstruction, LINEAR equations, INFORMATION networks

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders.

Autoren: Babaey, Vahid Faragardi, Hamid Reza

Quelle: Computers (2073-431X); Jun2025, Vol. 14 Issue 6, p205, 29p

Schlagwörter: LONG short-term memory, INFORMATION technology security, AUTOENCODERS, PLURALITY voting, PRIOR learning

Rejecting Unknown Gestures Based on Surface-Electromyography Using Variational Autoencoder.

Autoren: Dai, Qingfeng Wong, Yongkang Kankanhalli, Mohan et al.

Quelle: IEEE Transactions on Neural Systems & Rehabilitation Engineering; 2024, Vol. 32, p750-758, 9p

Schlagwörter: COMPUTER interfaces, AUTOENCODERS, GAUSSIAN distribution, FEATURE extraction, GESTURE

Mineralized-Anomaly Identification Based on Convolutional Sparse Autoencoder Network and Isolated Forest.

Autoren: Yang, Na Zhang, Zhenkai Yang, Jianhua et al.

Quelle: Natural Resources Research; Feb2023, Vol. 32 Issue 1, p1-18, 18p

Schlagwörter: ADDITION (Mathematics), GENERALIZATION, MINERALIZATION, ORE deposits, PRIOR learning

Geografische Kategorien: SHAANXI Sheng (China), CHINA

Parameter-adaptive variational autoencoder for linear/nonlinear blind source separation.

Autoren: Wei, Yuan-Hao Ni, Yi-Qing

Quelle: Journal of Civil Structural Health Monitoring; Apr2025, Vol. 15 Issue 4, p1161-1184, 24p

ERLD-HC: Entropy-Regularized Latent Diffusion for Harmony-Constrained Symbolic Music Generation.

Autoren: Li, Yang

Quelle: Entropy; Sep2025, Vol. 27 Issue 9, p901, 21p

Schlagwörter: HARMONICS (Music theory), AUTOENCODERS, SONGWRITING, MIDI (Standard), DEEP learning, PROBABILISTIC generative models, MACHINE learning, MUSICAL notation

CResDAE: A Deep Autoencoder with Attention Mechanism for Hyperspectral Unmixing.

Autoren: Zhao, Chong Wang, Jinlin Qiao, Qingqing et al.

Quelle: Remote Sensing; Nov2025, Vol. 17 Issue 21, p3622, 28p

Schlagwörter: AUTOENCODERS, GEOLOGICAL surveys, HYPERSPECTRAL imaging systems, SURFACE properties, REMOTE-sensing images, PROSPECTING

Heterogeneous Multi-Sensor Fusion for AC Motor Fault Diagnosis via Graph Neural Networks.

Autoren: Liao, Yuandong Li, Wenyong Lian, Guan et al.

Quelle: Electronics (2079-9292); May2025, Vol. 14 Issue 10, p2005, 24p

Schlagwörter: GRAPH neural networks, FAULT diagnosis, MULTISENSOR data fusion, ALTERNATING current electric motors, AUTOENCODERS

Rejecting Unknown Gestures Based on Surface-Electromyography Using Variational Autoencoder

Autoren: Qingfeng Dai Yongkang Wong Mohan Kankanhalli et al.

Quelle: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 32, Pp 750-758 (2024)

Schlagwörter: Gestures, gesture recognition, Electromyography, Recognition, Psychology, RM1-950, 02 engineering and technology, surface electromyography, Hand, Open-set recognition, Medical technology, 0202 electrical engineering, electronic engineering, information engineering, Humans, variational autoencoder, Therapeutics. Pharmacology, R855-855.5, Algorithms

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/38289843
https://doaj.org/article/cbd068913d444d699b46a584680c004f

A Deep Learning-Based Method for Mechanical Equipment Unknown Fault Detection in the Industrial Internet of Things.

Autoren: Liu, Xiaokai Meng, Xiangheng Ning, Lina et al.

Quelle: Sensors (14248220); Oct2025, Vol. 25 Issue 19, p5984, 26p

Schlagwörter: DEEP learning, FAULT diagnosis, INDUSTRY 4.0, CONVOLUTIONAL neural networks, AUTOENCODERS, LONG short-term memory, OUTLIER detection, MACHINE learning

Modeling Categorized Consumer Collections with Interlocked Hypergraph Neural Networks.

Autoren: Boughanmi, Khaled Ansari, Asim Li, Yang

Quelle: Journal of Marketing Research (JMR); Dec2025, Vol. 62 Issue 6, p1101-1124, 24p

Schlagwörter: CONSUMER preferences, COLLECTIONS, MUSIC industry, HYPERGRAPHS, ARTIFICIAL neural networks, DATA curation

Compression of EEG signals with the LSTM-autoencoder via domain adaptation approach.

Autoren: Liu, Yongfei Yang, Fan Wu, Binbin

Quelle: Computer Methods in Biomechanics & Biomedical Engineering; Sep2025, Vol. 28 Issue 12, p1857-1870, 14p

Schlagwörter: ARTIFICIAL neural networks, KNOWLEDGE transfer, DATA transmission systems, COMPUTER performance

Multichannel Variational Autoencoder-Based Speech Separation in Designated Speaker Order.

Autoren: Liao, Lele Cheng, Guoliang Ruan, Haoxin et al.

Quelle: Symmetry (20738994); Dec2022, Vol. 14 Issue 12, p2514, 13p

Schlagwörter: SPEECH, RECONSTRUCTION (Linguistics), SIGNAL reconstruction, INFORMATION resources, TEST methods

Random noise attenuation of ocean bottom seismometers based on a substep deep denoising autoencoder.

Autoren: Lin, Haoran Xu, Jian Xing, Lei et al.

Quelle: Geophysical Prospecting; May2024, Vol. 72 Issue 4, p1428-1441, 14p

Schlagwörter: OCEAN bottom, SEISMOMETERS, MICROSEISMS, SIGNAL-to-noise ratio, SUPERVISED learning, SPECTRUM analysis

Graph-informed convolutional autoencoder to classify brain responses during sleep.

Autoren: Zakeri, Sahar Makouei, Somayeh Danishvar, Sebelan

Quelle: Frontiers in Neuroscience; 2025, p1-24, 24p

Schlagwörter: CONVOLUTIONAL neural networks, SLEEP, RAPID eye movement sleep, AUDITORY perception, AUTOENCODERS

Domain-Adaptive Direction of Arrival (DOA) Estimation in Complex Indoor Environments Based on Convolutional Autoencoder and Transfer Learning.

Autoren: Shen, Lingyu Li, Jianfeng Pan, Jingjing et al.

Quelle: Sensors (14248220); May2025, Vol. 25 Issue 10, p2959, 24p

Schlagwörter: AUTOENCODERS, DEEP learning, FEATURE extraction, SMART homes, WIRELESS communications