Frequency selectivity via the SpEnt methodology for wideband speech compression

In speech and audio coding, frequency selectivity of the basis functions is an important property of the codec. The more precise the frequency selectivity, the less chance there is for audible coding effects due to uncanceled aliasing. We use Campbell´s (1960) coefficient rate and the spectral entropy (SpEnt) of the source random process as a guide to formulate adaptive nonuniform modulated lapped biorthogonal transforms (NMLBT). The use of the NMLBT allows for efficient implementation of a time-varying transform which possesses both good frequency and time resolution at all instances, without the need for transitional filters. By coupling the SpEnt methodology with modulated lapped biorthogonal transforms (MLBT), we develop band combining strategies to produce an adaptive NMLBT. Due to the nature of the SpEnt methodology, the new frequency selection process comprises a non-linear approximation method to determine the best n basis functions to represent the current speech frame. We implement a wideband speech compression scheme based on this strategy and verify its improved performance in coding speech and audio signals at 16 and 24 kbps