Classification of Cervix Lesions Using Filter Bank-Based Texture Mode

This paper focuses on a robust feature extraction algorithm for automatic classification of pathological and normal voices in noisy environments. The proposed algorithm is based on human auditory processing and the nonlinear Teager-Kaiser energy operator. The robust features which labeled Teager Energy Cepstrum Coefficients (TECCs) are computed in three steps. Firstly, each speech signal frame is passed through a Gammatone or Mel scale triangular filter bank. Then, the absolute value of the Teager energy operator of the short-time spectrum is calculated. Finally, the discrete cosine transform of the log-filtered Teager Energy spectrum is applied. This feature is proposed to identify the pathological voices using a developed neural system of multilayer perceptron (MLP). We evaluate the developed method using mixed voice database composed of recorded voice samples from normophonic or dysphonic speakers. In order to show the robustness of the proposed feature in detection of pathological voices at different White Gaussian noise levels, we compare its performance with results for clean environments. The experimental results show that TECCs computed from Gammatone filter bank are more robust in noisy environments than other extracted features, while their performance is practically similar to clean environments.

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Curve fitting, filter bank and wavelet feature fusion for classification of PCG signals

2016 24th Iranian Conference on Electrical Engineering (ICEE) ◽

10.1109/iraniancee.2016.7585518 ◽

2016 ◽

Cited By ~ 2

Author(s):

Maryam Imani ◽

Hassan Ghassemian

Keyword(s):

Curve Fitting ◽

Filter Bank ◽

Feature Fusion ◽

Pcg Signals

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Online Classification of Multiple Motor Imagery Tasks Using Filter Bank Based Maximum-a-Posteriori Common Spatial Pattern Filters

IRBM ◽

10.1016/j.irbm.2019.11.002 ◽

2020 ◽

Vol 41 (3) ◽

pp. 141-150

Author(s):

S.Z. Zahid ◽

M. Aqil ◽

M. Tufail ◽

M.S. Nazir

Keyword(s):

Spatial Pattern ◽

Motor Imagery ◽

Filter Bank ◽

Maximum A Posteriori ◽

A Posteriori ◽

Common Spatial Pattern ◽

Online Classification

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Taylor-Fourier filter bank Implemented with O-splines for the Detection and Classification of Faults

IEEE Transactions on Industrial Informatics ◽

10.1109/tii.2020.3010268 ◽

2020 ◽

pp. 1-1

Author(s):

Daniel Guillen ◽

Jose Antonio de la O Serna ◽

Alejandro Zamora-Mendez ◽

MRA Paternina ◽

Fernando Salinas

Keyword(s):

Filter Bank

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Features Extraction for Pollen Recognition Using Gabor Filters

Food Science and Applied Biotechnology ◽

10.30721/fsab2018.v1.i2.11 ◽

2018 ◽

Vol 1 (2) ◽

pp. 86

Author(s):

Dimitar Nikolov Nikolov ◽

Diana Dimitrova Tsankova

Keyword(s):

Filter Bank ◽

Recognition System ◽

Gabor Filters ◽

Features Extraction ◽

Botanical Origin ◽

Biologically Inspired ◽

Microscope Images ◽

European Pollen Database ◽

Components Analysis

The aim of the article is to investigate the features extraction from microscope images of pollens for a classification of honey on the base of its botanical origin. A filter-bank of Gabor filters (as a biologically inspired recognition system) is used to obtain features, which are then post-processed using normalization, down-sampling (by bicubic interpolation), and principal components analysis (PCA). PCA is used for reducing the features size and a proper visualization of the features extraction results. Microscope images from the European pollen database, including pollen images of linden, acacia, lavender, rapeseed, and thistle, are used to illustrate capabilities of the proposed features extraction approach. The performance of the proposed algorithm is evaluated by simulations in MATLAB environment.

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