Robust speech recognition using data-driven temporal filters based on independent component analysis

2004 ◽  
Author(s):  
Junhui Zhao ◽  
Jingming Kuang ◽  
Xiang Xie
Keyword(s):  
Speech Recognition ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Data Driven ◽  
Robust Speech Recognition ◽  
Using Data

scholarly journals Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA)

2018 ◽  
Vol 307 ◽  
pp. 125-137 ◽  
Author(s):  
Sebastian Michelmann ◽  
Matthias S. Treder ◽  
Benjamin Griffiths ◽  
Casper Kerrén ◽  
Frédéric Roux ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Data Driven ◽  
Intracranial Eeg

Independent component analysis based on data-driven reconstruction of multi-fault diagnosis

2017 ◽  
Vol 31 (12) ◽  
pp. e2932 ◽  
Author(s):  
Lin Feng ◽  
Yingwei Zhang ◽  
Xuguang Li ◽  
Yuanjian Fu
Keyword(s):  
Fault Diagnosis ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Data Driven

scholarly journals BLIND SOURCE SEPARATION (BSS) APPLIED TO SOUND IN VARIOUS CONDITIONS

2011 ◽  
Vol 14 (4) ◽  
pp. 34-42
Author(s):  
Quang Tan Truong ◽  
Huy Quang Tran ◽  
Phuong Huu Nguyen
Keyword(s):  
Speech Recognition ◽  
Independent Component Analysis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Component Analysis ◽  
Independent Component ◽  
Sound Sources ◽  
Sound Separation ◽  
Source Signals

Our ears often simultaneously receive various sound sources (speech, music, noise . . .), but we can still listen to the intended sound. A system of speech recognition must be able to achieve the same intelligent level. The problem is that we receive many mixed (combined) signals from many different source signals, and would like to recover them separately. This is the problem of Blind Source Separation (BSS). In the last decade or so a method has been developed to solve the above problem effectively, that is the Independent Component Analysis (ICA). There are many ICA algorithms for different applications. This report describes our application to sound separation when there are more sources than mixtures (underdetermined case). The results were quite good.

Data-driven tensor independent component analysis for model-based connectivity neurofeedback

NeuroImage ◽  
2019 ◽  
Vol 184 ◽  
pp. 214-226 ◽  
Author(s):  
Yury Koush ◽  
Nemanja Masala ◽  
Frank Scharnowski ◽  
Dimitri Van De Ville
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Data Driven ◽  
Model Based
Sign in / Sign up

Export Citation Format

Share Document