NON-STATIONARY SIGNAL CLASSIFICATION USING THE JOINT MOMENTS OF TIME-FREQUENCY DISTRIBUTIONS

摘要

We present a time-frequency based non-stationary time-series classification method which utilizes features derived from the joint moments of time-frequency distributions (TFDs). The method is applied to both synthetic and real signals, with comparison to classification performance utilizing features derived from temporal moments only and spectral moments only. The results show that a classification algorithm which utilizes joint time-frequency information, as quantified by the joint moments of the TFD, can improve performance over time or frequency-based features alone, for classification of non-stationary time series.