Time-frequency distribution kernel design over a discrete powers-of-two space

We introduce a new class of powers-of-two (PFT) kernels for fast real-time implementations of time-frequency (t-f) distributions. In this class, the local autocorrelation function is computed using a series of shifting and addition operations. PFT filter design techniques can be applied to produce fixed kernels or to design data-dependent kernels suitable for specific operating environments. In the t-f context, where the task is to identify the signal autoterms in the t-f domain, a discretized PFT kernel shows little or no difference in performance from its infinite precision counterpart.