Application of Spectral-Domain Matching and Pseudo Non-Linear Convolution to Down-Sample-Rate Conversion (DSRC)

摘要

A method of down-sample-rate conversion is discussed that exploits processes of spectral-domain matching and pseudo non-linear convolution applied to discrete data frames as an alternative to conventional convolutional filter and sub-sampling techniques. Spectral-domain matching yields a complex sample sequence that can subsequently be converted into a real sequence using the Discrete Hilhert Transform. The method is shown to result in substantially reduced time dispersion compared to the standard convolutional approach and circumvents filter symmetry selection such as linear phase or minimum phase. The formal analytic process is presented and validated through simulation then adapted to digital-audio sample-rate conversion by using a multi-frame overlap and add process. It has been tested in both LPCM-to-LPCM and DSD-to-LPCM applications where the latter can be simplified using a look-up code table.