Neural approach to time-frequency signal decomposition

Abstract

The problem of time-frequency decomposition of signals by means of neural networks has been investigated. The paper contains formalization of the problem as an optimization task followed by a proposition of recurrent neural network that can be used to solve it. Depending on the applied base functions, the neural network can be used for calculation of several standard time-frequency signal representations including Gabor. However, it can be especially useful in research on new signal decompositions with non-orthogonal bases as well as a part of feature extraction blocks in neural classification systems. The theoretic considerations have been illustrated by an example of analysis of a signal with time-varying parameters.