Results 81 to 90 of about 211 (100)

Enhanced independent vector analysis for audio separation in a room environment [PDF]

, 2013
Independent vector analysis (IVA) is studied as a frequency domain blind source separation method, which can theoretically avoid the permutation problem by retaining the dependency between different frequency bins of the same source vector while removing
Yanfeng Liang (7202699)
core  

Closest source selection using IVA and characteristic of mixing channel [PDF]

, 2009
This paper introduces a method for selecting a target source of interest. The target source is assumed to be the closest; to sensors among all the other sources regardless of the target source not being the dominant power at the sensors.
Yoo J.-K., Choi C.H., Lee S.-Y.
core   +1 more source

Enhanced independent vector analysis for speech separation in room environments [PDF]

, 2017
PhD ThesisThe human brain has the ability to focus on a desired sound source in the presence of several active sound sources. The machine based method lags behind in mimicking this particular skill of human beings.
Rafique, Waqas
core  

Robotic Sound Source Separation using Independent Vector Analysis [PDF]

, 2014
Beside haptic and vision, mobile robotic platforms are equipped with audition in order to autonomously navigate and interact with their environment. Speaker and speech recognition as well as the recognition of different kind of sounds are vital tasks for
Rothbucher, Martin; Denk, Christian; Reverchon, Martin; Shen, Hao; Diepold, Klaus
core  

Independent vector analysis for source separation using an energy driven mixed student\u27s T and super Gaussian source prior [PDF]

Independent vector analysis (IVA) can thoretically avoid the permutation problem in frequency domain blind source separation by using a multivariate source prior to retain the dependency between different frequency bins of each source. The performance of
Dlay SS, Rafique W, Naqvi SM, Chambers JA, Erateb S   +4 more
core  

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Brain Tissue Classification Using Independent Vector Analysis (IVA) for Magnetic Resonance Image

2009 Ninth IEEE International Conference on Bioinformatics and BioEngineering, 2009
The purpose of this study is to present a new method, independent vector analysis (IVA), by extending independent component analysis (ICA) of univariate source signals to multivariate source signals on Magnetic Resonance Imaging (MRI). IVA is utilized to relief the limitation of the conventional ICA approach.
Yaw-Jiunn Chiou, Hsian-Min Chen, Jyh Wen Chai, Clayton Chi-Chang Chen, Yen Chieh Ouyang, Wu-Chung Su, Ching-Wen Yang, San-Kan Lee, Chein-I Chang   +8 more
openaire   +3 more sources

Adaptive independent vector analysis for multi-subject complex-valued fMRI data [PDF]

Journal of Neuroscience Methods, 2017
Background Complex-valued fMRI data can provide additional insights beyond magnitude-only data. However, independent vector analysis (IVA), which has exhibited great potential for group analysis of magnitude-only fMRI data, has rarely been applied to ...
Xiao-Feng Gong, Vince Calhoun, Fengyu Cong   +2 more
exaly   +2 more sources

Constrained Independent Vector Analysis With Reference for Multi-Subject fMRI Analysis [PDF]

IEEE Transactions on Biomedical Engineering
Objective: Independent component analysis (ICA) is now a widely used solution for the analysis of multi-subject functional magnetic resonance imaging (fMRI) data. Independent vector analysis (IVA) generalizes ICA to multiple datasets (multi-subject data).
Hanlu Yang, Vince Calhoun, FRANCISCO Laport   +2 more
exaly   +2 more sources

Towards data-driven group inferences of resting-state fMRI data in rodents: Comparison of group ICA, GIG-ICA, and IVA-GL

Journal of Neuroscience Methods, 2022
Viktor Vegh, Xuan Vinh To, Fatima Nasrallah   +2 more
exaly  

Independent vector analysis: Model, applications, challenges

Pattern Recognition, 2023
Zhongqiang Luo
exaly