@inproceedings{chee2022performance,
author = {Chee, Jerry and Braun, Sebastian and Gopal, Vishak and Cutler, Ross},
title = {Performance optimizations on deep noise suppression models},
organization = {IEEE},
booktitle = {IEEE Workshop on Multimedia Signal Processing (MMSP)},
year = {2022},
month = {September},
abstract = {We study the role of magnitude structured pruning as an architecture search to speed up the inference time of a deep noise suppression (DNS) model. While deep learning approaches have been remarkably successful in enhancing audio quality, their increased complexity inhibits their deployment in real-time applications. We achieve up to a 7.25X inference speedup over the baseline, with a smooth model performance degradation. Ablation studies indicate that our
proposed network re-parameterization (i.e., size per layer) is the major driver of the speedup, and that magnitude structured pruning does comparably to directly training a model in the smaller size. We report inference speed because a parameter reduction does not necessitate speedup, and we measure model quality using an accurate non-intrusive objective speech quality metric},
url = {http://approjects.co.za/?big=en-us/research/publication/performance-optimizations-on-deep-noise-suppression-models/},
}