Time-frequency transforms in analysis of non-stationary quasi-periodic biomedical signal patterns for acoustic anomaly detection
Keywords:
system analysis, non-stationary quasi-periodic processes, pattern recognition, biomedical signal processing, ergatic systems, functional states, disruptive effect, time-frequency transforms, acoustic anomalies identification, frequency deviations, noiseAbstract
Introduction: New approaches to efficient compression and digital processing of audio signals are relevant today. There
is 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 quasiperiodic
biomedical signals in the problem of acoustic anomaly detection. Results: The study compared different time-frequency
transforms (such as windowed Fourier, Gabor, Wigner, pseudo Wigner, smoothed pseudo Wigner, Choi — Williams, Bertrand, pseudo
Bertrand, 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 of
two-dimensional representation and inverse transformation, real time processing, time-frequency transform quality, control
of time-frequency definition, time and frequency interference suppression, relative computational complexity, fast algorithm
implementation) for the problem of biomedial signal pattern recognition. A comparative table is presented with estimates of
information capacity for the considered time-frequency transforms. Practical relevance: The proposed approach can solve some
acoustic anomaly detection algorithm implementation problems common in non-stationary quasi-periodic processes, in order to
study disruptive effects causing a change in the functional state of ergatic system operators.