Time-frequency transforms in analysis of non-stationary quasi-periodic biomedical signal patterns for acoustic anomaly detection

Autor: Alexey Valerievich Bogomolov, Maxim Dmitrievich Alekhin, Anastasia Iskhakova
Rok vydání: 2020
Předmět:
Zdroj: Information and Control Systems. :15-23
ISSN: 2541-8610
1684-8853
DOI: 10.31799/1684-8853-2020-1-15-23
Popis: Introduction: New approaches to efficient compression and digital processing of audio signals are relevant today. Thereis a lot of interest to new pattern recognition methods which can improve the quality of acoustic anomaly detection. Purpose:Comparative analysis of methods for time-frequency transformation of audio signal patterns, including non-stationary quasiperiodicbiomedical signals in the problem of acoustic anomaly detection. Results: The study compared different time-frequencytransforms (such as windowed Fourier, Gabor, Wigner, pseudo Wigner, smoothed pseudo Wigner, Choi — Williams, Bertrand, pseudoBertrand, smoothed pseudo Bertrand, and wavelet transforms) based on systematization of their functional characteristics(such as the existence and limitedness of basis functions, presence of zero moments and biorthogonal form, opportunity oftwo-dimensional representation and inverse transformation, real time processing, time-frequency transform quality, controlof time-frequency definition, time and frequency interference suppression, relative computational complexity, fast algorithmimplementation) for the problem of biomedial signal pattern recognition. A comparative table is presented with estimates ofinformation capacity for the considered time-frequency transforms. Practical relevance: The proposed approach can solve someacoustic anomaly detection algorithm implementation problems common in non-stationary quasi-periodic processes, in order tostudy disruptive effects causing a change in the functional state of ergatic system operators.
Databáze: OpenAIRE