scholarly journals Investigation of a Method for EEG Signal De-Noising Based on the DIVA Model

2020 ◽  
Author(s):  
Shaobai Zhang ◽  
Lihong Jiao ◽  
Ningning Zhou
Keyword(s):  
Input Signal ◽  
Speech Processing ◽  
Vocal Tract ◽  
Model Simulation ◽  
Matching Pursuit ◽  
Eeg Signal ◽  
Transform Method ◽  
Sparse Decomposition ◽  
Power Frequency ◽  
Diva Model

The DIVA (Directions Into Velocities of Articulators) model is an adaptive neural network model that is used to control the movement of the analog vocal tract to generate words, syllables, or phonemes. The input signal to the DIVA model is the EEG (electroencephalogram) signal acquired from the human brain. However, due to the influence of power frequency interference and other forms of noise, the input signal can be non-stationary and can also contain a variety of multi-form waveforms in its instantaneous structure. Input of such a signal into the DIVA model affects normal speech processing. Therefore, based on the concept of sparse decomposition, this paper applies and improves an adaptive sparse decomposition model for feature extraction of the general EEG signal structure and then uses the Matching Pursuit algorithm to compute the optimal atom. The original EEG signal can then be represented by atoms in a complete atomic library. This model removes noise from the EEG signal resulting in a better signal than the wavelet transform method. Finally, applies the EEG signal de-noised by this model to DIAV model. Simulation results show that the method improves phonetic pronunciation greatly.

Unattended speech processing: Effect of vocal-tract length

2007 ◽  
Vol 121 (2) ◽  
pp. EL90-EL95 ◽  
Author(s):  
Marie Rivenez ◽  
Christopher J. Darwin ◽  
Léonore Bourgeon ◽  
Anne Guillaume
Keyword(s):  
Speech Processing ◽  
Vocal Tract ◽  
Tract Length ◽  
Processing Effect

Feature Extraction Methods for Speaker Recognition: A Review

2017 ◽  
Vol 31 (12) ◽  
pp. 1750041 ◽  
Author(s):  
Gopal Chaudhary ◽  
Smriti Srivastava ◽  
Saurabh Bhardwaj
Keyword(s):  
Feature Extraction ◽  
Speech Processing ◽  
Speaker Recognition ◽  
Vocal Tract ◽  
Recognition System ◽  
Extraction Methods ◽  
Future Research ◽  
Time Variability ◽  
Noise Compensation ◽  
Clear Vision

This paper presents main paradigms of research for feature extraction methods to further augment the state of art in speaker recognition (SR) which has been recognized extensively in person identification for security and protection applications. Speaker recognition system (SRS) has become a widely researched topic for the last many decades. The basic concept of feature extraction methods is derived from the biological model of human auditory/vocal tract system. This work provides a classification-oriented review of feature extraction methods for SR over the last 55 years that are proven to be successful and have become the new stone to further research. Broadly, the review work is dichotomized into feature extraction methods with and without noise compensation techniques. Feature extraction methods without noise compensation techniques are divided into following categories: On the basis of high/low level of feature extraction; type of transform; speech production/auditory system; type of feature extraction technique; time variability; speech processing techniques. Further, feature extraction methods with noise compensation techniques are classified into noise-screened features, feature normalization methods, feature compensation methods. This classification-oriented review would endow the clear vision of readers to choose among different techniques and will be helpful in future research in this field.

Two dictionaries matching pursuit for sparse decomposition of signals

2006 ◽  
Vol 86 (11) ◽  
pp. 3472-3480 ◽  
Author(s):  
Peng Xu ◽  
Dezhong Yao
Keyword(s):  
Matching Pursuit ◽  
Sparse Decomposition

Power frequency noise suppression of magnetotelluric data based on frequency-domain sparse decomposition

2017 ◽  
Author(s):  
Guang Li ◽  
Jingtian Tang ◽  
Cong Zhou ◽  
Xiao Xiao ◽  
Shangshi Wen
Keyword(s):  
Frequency Domain ◽  
Noise Suppression ◽  
Frequency Noise ◽  
Magnetotelluric Data ◽  
Sparse Decomposition ◽  
Power Frequency

A highly accurate and fast power quality disturbances classification based on dictionary learning sparse decomposition

2018 ◽  
Vol 41 (1) ◽  
pp. 145-155 ◽  
Author(s):  
Delong Cai ◽  
Kaicheng Li ◽  
Shunfan He ◽  
Yuanzheng Li ◽  
Yi Luo
Keyword(s):  
Computational Complexity ◽  
Power Quality ◽  
Dictionary Learning ◽  
Adaptive Algorithm ◽  
Classification Accuracy ◽  
Matching Pursuit ◽  
Sparse Decomposition ◽  
Fast Power ◽  
Good Improvement ◽  
Power Quality Disturbances

This paper proposes a highly accurate and fast power quality disturbances (PQDs) classification using dictionary learning sparse decomposition (DLSD). Firstly, an over-complete dictionary is constructed by combining an identity matrix with a learning dictionary trained by K-SVD algorithm. Secondly, the features and the fuzzy primary classifications of PQDs are obtained by calculating the sparse decomposition coefficients based on the learning dictionary. For being adaptive to sparsity and reducing computational complexity, a fast adaptive matching pursuit (FAMP) using sparsity adaptive algorithm and regularized atom selection is proposed. Then, a decision tree is adopted to accomplish accurate classification by using the estimated features and the pre-classification results. Finally, the proposed approach is tested by PQDs from simulations, IEEE PES database and actual measurements. Moreover, several testing signals, which contain strong noise and frequency deviation, are introduced to further validate DLSD. The results demonstrate that DLSD has a good improvement on computational complexity and classification accuracy when dealing with PQDs classification.

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