Augmented TDNN for frequency and scale invariant sequence classification
Neural networks are now widely used for pattern classification. But research on how to obtain frequency and scale invariance for sequence classification is lacking. In this paper, we show that sequence classification is transformed to multi-dimensional curve classification by time-delay neural network (TDNN). An augmented TDNN (ATDNN) which builds frequency and scale induction functionals into TDNN is then developed and proved to be a frequency and scale invariant sequence classifier. In ATDNN, adaptive time-delay units are used to capture the temporal relationships between events. The sampling of the time-delay units is controlled by frequency and scale induction functionals. The preprocessed signal is then discriminated by a neural network classifier. The principal advantage of this architecture is that the invariances are built directly into the architecture and need not be learned. To assess the performance of the proposed architecture, we apply it to the problem of signal detection in noisy background as well as sequence classification. Simulations show that the proposed architecture is very effective in analyzing sequences that are subject to large frequency and scale variance.
frequency and scale invariance sequence classification time-delay neural network