Sub-dictionary selection using local discriminant bases algorithm for signal classification

In signal decompositions using overcomplete, redundant time-frequency (TF) dictionaries, often it is challenging to restrict the dictionary to a sub-dictionary tailored for specific applications. In the proposed technique we used a similar approach as local discriminant bases algorithm (LDB) to select optimal TF subdictionaries for signal classification applications. A novel time-width versus frequency band mapping was generated for each of the signal class. These mappings of different classes were compared using a discriminant measure to arrive at a sub-dictionary. This sub-dictionary was then used for decomposing the testing set signals, followed by feature extraction and classification. Two highly nonstationary biomedical databases (1) vibroarthrographic signals (89 signals, 51 normal and 38 abnormal) (2) pathological speech database (100 signals, 50 normal and 50 pathological) were tested. Classification accuracies as high as 74.2% and 92% were achieved respectively. Due to the sub-dictionary approach, approximately a 40% reduction in signal decomposition time was observed for the tested databases.