Results 31 to 40 of about 34,052 (223)
Speaker verification using sequence discriminant support vector machines [PDF]
, 2005 This paper presents a text-independent speaker verification system using support vector machines (SVMs) with score-space kernels. Score-space kernels generalize Fisher kernels and are based on underlying generative models such as Gaussian mixture models (
Renals, S., Wan, V.
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POLARIMETRIC SAR DATA GMM CLASSIFICATION BASED ON IMPROVED FREEMAN INCOHERENT DECOMPOSITION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 Due to the increasing volume of available SAR Data, powerful classification processings are needed to interpret the images. GMM (Gaussian Mixture Model) is widely used to model distributions.
S. Rouabah, M. Ouarzeddine, B. Azmedroub
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CGMVAE: Coupling GMM Prior and GMM Estimator for Unsupervised Clustering and Disentanglement
IEEE Access, 2021 Impressive progress has been recently witnessed on deep unsupervised clustering and feature disentanglement. In this paper, we propose a novel method on top of one recent architecture with a novel explanation of Gaussian mixture model (GMM) membership ...
Chunzhi Gu +3 more
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Jixie qiangdu, 2023 In order to study the frequency domain calculation method of non-Gaussian excitation, Gaussian mixture model（GMM） is introduced. The measured non-Gaussian excitation is transformed into probabilistic power spectrum（PPSD） through GMM model, so that the ...
XU Yang +3 more
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Sliced Wasserstein Distance for Learning Gaussian Mixture Models
, 2017 Gaussian mixture models (GMM) are powerful parametric tools with many applications in machine learning and computer vision. Expectation maximization (EM) is the most popular algorithm for estimating the GMM parameters.
Hoffmann, Heiko +2 more
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Recognition of Aircraft Engine Sound Based on GMM-UBM Model
MATEC Web of Conferences, 2017 Gaussian mixture model-universal background model (GMM-UBM) is a commonly-used speaker recognition technology, and which has achieved good effect for detection speaker’s sound.
Yuan Shuai, Sun Chengli, Yang Haoge
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Effective Learning of a GMRF Mixture Model
IEEE Access, 2022 Learning a Gaussian Mixture Model (GMM) is hard when the number of parameters is too large given the amount of available data. As a remedy, we propose restricting the GMM to a Gaussian Markov Random Field Mixture Model (GMRF-MM), as well as a new method ...
Shahaf E. Finder +2 more
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Advanced Functional Materials, EarlyView.In MOCVD MoS2 memristors, a current compliance‐regulated Ag filament mechanism is revealed. The filament ruptures spontaneously during volatile switching, while subsequent growth proceeds vertically through the MoS2 layers and then laterally along the van der Waals gaps during nonvolatile switching.
Yuan Fa +19 more
wiley +1 more source
Speaker Ｒecognition under Limited Data
Journal of Harbin University of Science and Technology, 2017 Speaker recognition has attracted broad and deep research in the past few decades，and manymethods have been proposed. At present，the popular methods such as the Gaussian mixture model-Universal background model( GMM-UBM) and Gaussian mixture model ...
GAI Chao-xu +2 more
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Genome Biology, 2020 Identifying and removing multiplets are essential to improving the scalability and the reliability of single cell RNA sequencing (scRNA-seq). Multiplets create artificial cell types in the dataset.
Hongyi Xin +12 more
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