Signal Useful Information Recovery by Overlapping Supports of Time-Frequency Representations

This paper presents a novel method for automatic extraction of useful information from time-frequency distributions of noisy signals. Signals' features are examined through the combined analysis of their time-frequency energy distributions and inverse complexity maps. The inverse complexity approach gives a new entropy-based insight into the signal structure. These two approaches result in mostly disjoint time-frequency supports, overlapping only in the proximity of the signal components. Locations of the signal components (i.e., useful information) are thus identified by low complexity and high amplitude occurring together in the time-frequency plane. Compared to existing methods using only the time-frequency energy distribution, useful information extraction by the proposed method exhibits a significantly reduced error rate.