A novel time-frequency feature extraction algorithm based on dictionary learning
Time-frequency (TF) representations have been widely used over the past decade to characterize the non-stationary content of signals in the joint time and frequency domain. Although a number of effective TF analysis methods based on wavelet or Gabor transform have been developed, these methods use pre-determined basis functions and still require feature extraction methods to reduce redundancy and preserve important TF information related to the application of interest. This paper explores a novel TF feature extraction algorithm using a modified dictionary learning approach. The proposed algorithm is developed to modify learned dictionaries and derive TF features unique to each class. It emulates the way joint dictionary learning algorithms use common dictionaries to promote discrimination between the data from different classes, thereby allowing for an improved analysis of complex and multitask data. The proposed method indicated a significant performance in identification of the discriminant vs. common structures of the TF data.