An efficient filter bank architecture for the cross-term reduced processing of discrete time-frequency distributions

Time-frequency distributions (TFDs) based on Cohen´s class have significant potential for the analysis of nonstationary signals. By employing a linear orthogonal signal expansion followed by nonlinear thresholding cross-terms, it has been shown that the TFD visuality is improved. The main reason for the limited used of TFDs in on-line applications is however their high computational requirement. In this paper, we present a filter bank based architecture which implements efficiently the cross-term reduced computation of TFDs. Its incremental refinement feature allows especially dead-line based real-time processing of time-critical applications. Moreover, the multirate computing provides a vehicle to operate at a reduced rate while still maintaining the overall throughput. The derived architecture is modular with respect to the number of frequencies to be evaluated, the cross-term reduction degree, and the choice of basis filters. The complexity analysis shows that the proposed architecture can indeed reduce the computation burden. Further, the algorithm can be flexibly tailored to available a-priori knowledge about the signal statistics. The regular structure and the underlying multirate FIR filter bank are suitable for VLSI integration