Signal-adapted wavelet filter bank design

A few researchers have tried to find the measurement of the reverberation time of a passenger car. However, this has proved to be extremely difficult because the reverberation time of a passenger car is too short to measure using the traditional bandpass filter. If the reverberation time is very short, the product of the reverberation time ( T) and the bandwidth ( B) of the traditional bandpass filter is very small. The low level of the product BT required for the measurement of the reverberation time using the traditional bandpass filter is 16. In order to overcome this problem, the wavelet filter bank has been developed. In the paper, this new wavelet filter is employed to measure the reverberation times of five different classes of passenger car. The low level of the product BT required for the measurement of reverberation time using the wavelet filter is 4. Therefore, it was possible to measure the reverberation times of five passenger cars successfully using the new wavelet filter bank. It is found that the reverberation times measured in most passenger cars are around 0.04. It is a very short reverberation time compared with those of general acoustic rooms like a concert hall.

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Classification of Mimetite Spectral Signatures using Orthogonal Subspace Projection with Complex Wavelet Filter Bank based Dimensionality Reduction

Communication and Power Engineering ◽

10.1515/9783110469608-012 ◽

2016 ◽

pp. 126-131 ◽

Cited By ~ 1

Keyword(s):

Dimensionality Reduction ◽

Filter Bank ◽

Wavelet Filter ◽

Subspace Projection ◽

Spectral Signatures ◽

Wavelet Filter Bank ◽

Orthogonal Subspace ◽

Orthogonal Subspace Projection ◽

Complex Wavelet

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A New Transient Model for Three-Phase Power Transformers Using a Wavelet Filter Bank

IEEE Transactions on Power Delivery ◽

10.1109/tpwrd.2004.838636 ◽

2005 ◽

Vol 20 (2) ◽

pp. 1409-1416 ◽

Cited By ~ 12

Author(s):

S.A. Saleh ◽

M.A. Rahman

Keyword(s):

Filter Bank ◽

Power Transformers ◽

Wavelet Filter ◽

Transient Model ◽

Three Phase ◽

Wavelet Filter Bank

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Time–frequency localization using three-tap biorthogonal wavelet filter bank for electrocardiogram compressions

Biomedical Engineering Letters ◽

10.1007/s13534-019-00117-9 ◽

2019 ◽

Vol 9 (3) ◽

pp. 407-411 ◽

Cited By ~ 6

Author(s):

Ashish Kumar ◽

Rama Komaragiri ◽

Manjeet Kumar

Keyword(s):

Filter Bank ◽

Wavelet Filter ◽

Biorthogonal Wavelet ◽

Time Frequency ◽

Wavelet Filter Bank ◽

Time Frequency Localization

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Hypertension Diagnosis Index for Discrimination of High-Risk Hypertension ECG Signals Using Optimal Orthogonal Wavelet Filter Bank

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16214068 ◽

2019 ◽

Vol 16 (21) ◽

pp. 4068 ◽

Cited By ~ 10

Author(s):

Jaypal Singh Rajput ◽

Manish Sharma ◽

U. Rajendra Acharya

Keyword(s):

Blood Pressure ◽

High Risk ◽

Filter Bank ◽

Early Stage ◽

Fractional Dimension ◽

Wavelet Filter ◽

Orthogonal Wavelet ◽

Ecg Signals ◽

Wavelet Filter Bank ◽

Hypertension Diagnosis

Hypertension (HT) is an extreme increment in blood pressure that can prompt a stroke, kidney disease, and heart attack. HT does not show any symptoms at the early stage, but can lead to various cardiovascular diseases. Hence, it is essential to identify it at the beginning stages. It is tedious to analyze electrocardiogram (ECG) signals visually due to their low amplitude and small bandwidth. Hence, to avoid possible human errors in the diagnosis of HT patients, an automated ECG-based system is developed. This paper proposes the computerized segregation of low-risk hypertension (LRHT) and high-risk hypertension (HRHT) using ECG signals with an optimal orthogonal wavelet filter bank (OWFB) system. The HRHT class is comprised of patients with myocardial infarction, stroke, and syncope ECG signals. The ECG-data are acquired from physionet’s smart health for accessing risk via ECG event (SHAREE) database, which contains recordings of a total 139 subjects. First, ECG signals are segmented into epochs of 5 min. The segmented epochs are then decomposed into six wavelet sub-bands (WSBs) using OWFB. We extract the signal fractional dimension (SFD) and log-energy (LOGE) features from all six WSBs. Using Student’s t-test ranking, we choose the high ranked WSBs of LOGE and SFD features. We develop a novel hypertension diagnosis index (HDI) using two features (SFD and LOGE) to discriminate LRHT and HRHT classes using a single numeric value. The performance of our developed system is found to be encouraging, and we believe that it can be employed in intensive care units to monitor the abrupt rise in blood pressure while screening the ECG signals, provided this is tested with an extensive independent database.

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