Spectral distance measure applied to the optimum design of DPCM coders with L predictors

This paper explores the intermediate solutions between fixed prediction and forward adaptative prediction in ADPCM which consists of using a finite number, L, of preselected linear predictors of order M. The design problem of selecting the optimum set of predictors with respect to the overall prediction gain is formulated and an iterative procedure is described to obtain the solutions. The relative prediction-gain improvement is computed for a 3 sec. speech sample and for several values of L,M, and block size showing that of the adaptative over fixed-prediction improvement in dB is reached with only and 2/3 with . The design problem solved by minimizing Itakura distance is shown to yield essentially identical performances. A linear discriminant property in the autocorrelation space is pointed out. Based on that property a pattern classification approach is proposed as an hardware-efficient coding algorithm.