Spectral entropy: an alternative indicator for rate allocation?

We introduce an approach to speech segment classification that differs from the usual energy, correlation, and zero-crossing criteria. Instead, we measure the gross shape of the short-term speech spectrum using spectral entropy to derive some indication of effective bandwidth. We propose to lower the required encoding rate by compensating for dynamic variations in signal bandwidth. We show that the spectral entropy can be used effectively to determine regions of voicing activity even in extreme background noise