Subspectral modeling in filter banks

The article deals with spectral modeling in filter banks. It is shown, both theoretically and experimentally, that subspectral modeling is superior to full spectrum modeling if performed before the rate change. The price paid for this performance improvement is an increase of computations. A few different signal sources were considered in this study. It is shown that the performance of AR and ARMA techniques are comparable in subspectral modeling. The first is desired because of its simplicity. As an application of this study, we implemented a CELP based speech codec embedded in a filter bank structure. We found that there were no performance improvements of subband CELP technique over the fullband case. The theoretical reasonings of the experimental results are also given