Near-Perfect Reconstruction Oversampled Nonuniform Cosine-Modulated Filter Banks Based on Frequency Warping and Subband Merging

2012 ◽  
Vol 58 (2) ◽  
pp. 177-192 ◽  
Author(s):  
Marek Parfieniuk ◽  
Alexander Petrovsky
Keyword(s):  
Group Delay ◽  
Filter Banks ◽  
Main Idea ◽  
Perceptual Processing ◽  
Perfect Reconstruction ◽  
Frequency Warping ◽  
Small Loss ◽  
Novel Method ◽  
The One ◽  
Near Perfect Reconstruction

Near-Perfect Reconstruction Oversampled Nonuniform Cosine-Modulated Filter Banks Based on Frequency Warping and Subband MergingA novel method for designing near-perfect reconstruction oversampled nonuniform cosine-modulated filter banks is proposed, which combines frequency warping and subband merging, and thus offers more flexibility than known techniques. On the one hand, desirable frequency partitionings can be better approximated. On the other hand, at the price of only a small loss in partitioning accuracy, both warping strength and number of channels before merging can be adjusted so as to minimize the computational complexity of a system. In particular, the coefficient of the function behind warping can be constrained to be a negative integer power of two, so that multiplications related to allpass filtering can be replaced with more efficient binary shifts. The main idea is accompanied by some contributions to the theory of warped filter banks. Namely, group delay equalization is thoroughly investigated, and it is shown how to avoid significant aliasing by channel oversampling. Our research revolves around filter banks for perceptual processing of sound, which are required to approximate the psychoacoustic scales well and need not guarantee perfect reconstruction.

Autoencoder for ecg signal outlier processing in system of biometric authentication

2019 ◽  
Vol 24 (1-2) ◽  
pp. 108-117
Author(s):  
Khoma V.V. ◽  
◽  
Khoma Y.V. ◽  
Khoma P.P. ◽  
Sabodashko D.V. ◽  
...  
Keyword(s):  
Neural Networks ◽  
Statistical Approach ◽  
Main Idea ◽  
Biometric Identification ◽  
Ecg Signal ◽  
Biometric Authentication ◽  
Lower Accuracy ◽  
Novel Method ◽  
Waveform Distortions ◽  
The One

A novel method for ECG signal outlier processing based on autoencoder neural networks is presented in the article. Typically, heartbeats with serious waveform distortions are treated as outliers and are skipped from the authentication pipeline. The main idea of the paper is to correct these waveform distortions rather them in order to provide the system with better statistical base. During the experiments, the optimum autoencoder architecture was selected. An open Physionet ECGID database was used to verify the proposed method. The results of the studies were compared with previous studies that considered the correction of anomalies based on a statistical approach. On the one hand, the autoencoder shows slightly lower accuracy than the statistical method, but it greatly simplifies the construction of biometric identification systems, since it does not require precise tuning of hyperparameters.

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