Comparison of six envelope extraction methods based on abnormal heart sounds

2010 ◽  
Author(s):  
Lisheng Xu ◽  
Ying Wang ◽  
Yupeng Yao ◽  
Cong Feng ◽  
Yue Zhao ◽  
...  
Keyword(s):  
Extraction Methods ◽  
Heart Sounds ◽  
Envelope Extraction

COMPARISON BETWEEN DISCRETE AND PACKET WAVELET TRANSFORM ANALYSES IN THE STUDY OF HEARTBEAT CARDIAC SOUNDS

2007 ◽  
Vol 07 (02) ◽  
pp. 199-214 ◽  
Author(s):  
S. M. DEBBAL ◽  
F. BEREKSI-REGUIG
Keyword(s):  
Wavelet Transform ◽  
Heart Sound ◽  
Wavelet Packet ◽  
Extraction Methods ◽  
Heart Sounds ◽  
Discrete Wavelet ◽  
Clinical Tool ◽  
Prognostic Information ◽  
Transform Method ◽  
Time Frequency

This work investigates the study of heartbeat cardiac sounds through time–frequency analysis by using the wavelet transform method. Heart sounds can be utilized more efficiently by medical doctors when they are displayed visually rather through a conventional stethoscope. Heart sounds provide clinicians with valuable diagnostic and prognostic information. Although heart sound analysis by auscultation is convenient as a clinical tool, heart sound signals are so complex and nonstationary that they are very difficult to analyze in the time or frequency domain. We have studied the extraction of features from heart sounds in the time–frequency (TF) domain for the recognition of heart sounds through TF analysis. The application of wavelet transform (WT) for heart sounds is thus described. The performances of discrete wavelet transform (DWT) and wavelet packet transform (WP) are discussed in this paper. After these transformations, we can compare normal and abnormal heart sounds to verify the clinical usefulness of our extraction methods for the recognition of heart sounds.

The effect of different extraction methods on chemical composition profile and pharmacological activities of essential oils and hydrosols

Planta Medica ◽  
2016 ◽  
Vol 81 (S 01) ◽  
pp. S1-S381
Author(s):  
F Ghavidel ◽  
MM Zarshenas ◽  
A Sakhteman ◽  
A Gholami ◽  
Y Ghasemi ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Essential Oils ◽  
Extraction Methods ◽  
Composition Profile ◽  
Pharmacological Activities

Characterization of dissolved organic matter (DOM) extracted from soils by hot water percolation (HWP)

2010 ◽  
Vol 59 (1) ◽  
pp. 99-108 ◽  
Author(s):  
M. Takács ◽  
Gy. Füleky
Keyword(s):  
Organic Matter ◽  
Humic Acid ◽  
Natural Organic Matter ◽  
Extraction Methods ◽  
Soil Samples ◽  
Hot Water ◽  
Soil Extracts ◽  
Water Percolation ◽  
Soil Humic Acid

The Hot Water Percolation (HWP) technique for preparing soil extracts has several advantages: it is easily carried out, fast, and several parameters can be measured from the same solution. The object of this study was to examine the possible use of HWP extracts for the characterization of soil organic matter. The HPLC-SEC chromatograms, UV-VIS and fluorescence properties of the HWP extracts were studied and the results were compared with those of the International Humic Substances Society (IHSS) Soil Humic Acid (HA), IHSS Soil Fulvic Acid (FA) and IHSS Suwannee Natural Organic Matter (NOM) standards as well as their HA counterparts isolated by traditional extraction methods from the original soil samples. The DOM of the HWP solution is probably a mixture of organic materials, which have some characteristics similar to the Soil FA fractions and NOM. The HWP extracted organic material can be studied and characterized using simple techniques, like UV-VIS and fluorescence spectroscopy.

New Feature Vectors using GFCC for Speaker Identification

2018 ◽  
Vol 6 (8) ◽  
pp. 243
Author(s):  
A. Nagesh
Keyword(s):  
Speaker Recognition ◽  
Speaker Identification ◽  
Signal To Noise Ratio ◽  
Main Idea ◽  
Extraction Methods ◽  
Identification System ◽  
Identification Performance ◽  
Feature Vectors ◽  
Overall Performance ◽  
New Feature

The feature vectors of speaker identification system plays a crucial role in the overall performance of the system. There are many new feature vectors extraction methods based on MFCC, but ultimately we want to maximize the performance of SID system.  The objective of this paper to derive Gammatone Frequency Cepstral Coefficients (GFCC) based a new set of feature vectors using Gaussian Mixer model (GMM) for speaker identification. The MFCC are the default feature vectors for speaker recognition, but they are not very robust at the presence of additive noise. The GFCC features in recent studies have shown very good robustness against noise and acoustic change. The main idea is  GFCC features based on GMM feature extraction is to improve the overall speaker identification performance in low signal to noise ratio (SNR) conditions.

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