Application of Spectral-Domain Matching and Pseudo Non-Linear Convolution to Down-Sample-Rate-Conversion (DSRC)
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MA. J.. Hawksford, "Application of Spectral-Domain Matching and Pseudo Non-Linear Convolution to Down-Sample-Rate-Conversion (DSRC)," Paper 9811, (2017 October.). doi:
MA. J.. Hawksford, "Application of Spectral-Domain Matching and Pseudo Non-Linear Convolution to Down-Sample-Rate-Conversion (DSRC)," Paper 9811, (2017 October.). doi:
Abstract: 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 Hilbert 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.
@article{hawksford2017application,
author={hawksford, malcolm o. j.},
journal={journal of the audio engineering society},
title={application of spectral-domain matching and pseudo non-linear convolution to down-sample-rate-conversion (dsrc)},
year={2017},
volume={},
number={},
pages={},
doi={},
month={october},}
@article{hawksford2017application,
author={hawksford, malcolm o. j.},
journal={journal of the audio engineering society},
title={application of spectral-domain matching and pseudo non-linear convolution to down-sample-rate-conversion (dsrc)},
year={2017},
volume={},
number={},
pages={},
doi={},
month={october},
abstract={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 hilbert 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. },}
TY - paper
TI - Application of Spectral-Domain Matching and Pseudo Non-Linear Convolution to Down-Sample-Rate-Conversion (DSRC)
SP -
EP -
AU - Hawksford, Malcolm O. J.
PY - 2017
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2017
TY - paper
TI - Application of Spectral-Domain Matching and Pseudo Non-Linear Convolution to Down-Sample-Rate-Conversion (DSRC)
SP -
EP -
AU - Hawksford, Malcolm O. J.
PY - 2017
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2017
AB - 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 Hilbert 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.
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 Hilbert 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.
Author:
Hawksford, Malcolm O. J.
Affiliation:
University of Essex, Colchester, Essex, UK
AES Convention:
143 (October 2017)
Paper Number:
9811
Publication Date:
October 8, 2017Import into BibTeX
Subject:
Signal Processing
Permalink:
http://www.aes.org/e-lib/browse.cfm?elib=19208