A signal classification approach using time-width vs frequency band sub-energy distributions

Time-frequency (TF) signal decompositions provide us with ample information and extreme flexibility for signal analysis. By applying suitable processing on the TF decomposition parameters, even subtle signal characteristics can be revealed. In many real world applications, identification of these subtle differences make a significant impact in signal analysis. Particularly in classification applications using TF approaches, there may be situations where a localized high discriminative signal structure is diluted due to the presence of other overlapping signal structures. To address this problem we propose a novel approach to construct multiple time-width vs frequency band mappings based on the energy decomposition pattern of the signal. These mapping are then analyzed to locate the highly discriminative features for classification. Initial results with two real world biomedical signal databases: (1) vibroarthrographic (VAG) signals; and (2) pathological speech signals, indicate high potential for the proposed technique.