Computing discrete time-frequency distributions using principal component filter bank

Time frequency distributions (TFDs) based on Cohen's (1995) class have significant potential for the analysis of non-stationary signals. However, the high computational requirement limits their use in systems when hardware resources are costly. Independently of TFDs, developments in wavelets and filter bank theory have led to the design of a signal-adapted principal component filter bank so that good L/sup 2/ approximations to the signal can be computed from a reduced number of channels. In this paper, we integrate these two different but complementary works into a novel cross-term reduced scheme for the TFD computation with incremental refinement feature. The approach allows dead-line based real-time processing of time-critical applications. Further, the polyphase formulation of the filter bank yields parallel structures at reduced operation rate for the same throughput which can be advantageous for VLSI integration in terms of local connections and low power consumption.